fake news in social media research paper

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here .

Loading metrics

Open Access

Peer-reviewed

Research Article

A systematic review on fake news research through the lens of news creation and consumption: Research efforts, challenges, and future directions

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliation School of Intelligence Computing, Hanyang University, Seoul, Republic of Korea

Roles Conceptualization, Formal analysis, Investigation, Methodology, Supervision, Writing – original draft, Writing – review & editing

Affiliation College of Information Sciences and Technology, Pennsylvania State University, State College, PA, United States of America

Roles Funding acquisition, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

• Bogoan Kim, 
• Aiping Xiong, 
• Dongwon Lee, 
• Kyungsik Han

• Published: December 9, 2021
• https://doi.org/10.1371/journal.pone.0260080
• Reader Comments

28 Dec 2023: The PLOS One Staff (2023) Correction: A systematic review on fake news research through the lens of news creation and consumption: Research efforts, challenges, and future directions. PLOS ONE 18(12): e0296554. https://doi.org/10.1371/journal.pone.0296554 View correction

Although fake news creation and consumption are mutually related and can be changed to one another, our review indicates that a significant amount of research has primarily focused on news creation. To mitigate this research gap, we present a comprehensive survey of fake news research, conducted in the fields of computer and social sciences, through the lens of news creation and consumption with internal and external factors.

We collect 2,277 fake news-related literature searching six primary publishers (ACM, IEEE, arXiv, APA, ELSEVIER, and Wiley) from July to September 2020. These articles are screened according to specific inclusion criteria (see Fig 1). Eligible literature are categorized, and temporal trends of fake news research are examined.

As a way to acquire more comprehensive understandings of fake news and identify effective countermeasures, our review suggests (1) developing a computational model that considers the characteristics of news consumption environments leveraging insights from social science, (2) understanding the diversity of news consumers through mental models, and (3) increasing consumers’ awareness of the characteristics and impacts of fake news through the support of transparent information access and education.

We discuss the importance and direction of supporting one’s “digital media literacy” in various news generation and consumption environments through the convergence of computational and social science research.

Citation: Kim B, Xiong A, Lee D, Han K (2021) A systematic review on fake news research through the lens of news creation and consumption: Research efforts, challenges, and future directions. PLoS ONE 16(12): e0260080. https://doi.org/10.1371/journal.pone.0260080

Editor: Luigi Lavorgna, Universita degli Studi della Campania Luigi Vanvitelli, ITALY

Received: March 24, 2021; Accepted: November 2, 2021; Published: December 9, 2021

Copyright: © 2021 Kim et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript.

Funding: This research was supported by the Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (2019-0-01584, 2020-0-01373).

Competing interests: The authors have declared that no competing interests exist.

1 Introduction

The spread of fake news not only deceives the public, but also affects society, politics, the economy and culture. For instance, Buzzfeed ( https://www.buzzfeed.com/ ) compared and analyzed participation in 20 real news and 20 fake news articles (e.g., likes, comments, share activities) that spread the most on Facebook during the last three months of the 2016 US Presidential Election. According to the results, the participation rate of fake news (8.7 million) was higher than that of mainstream news (7.3 million), and 17 of the 20 fake news played an advantageous role in winning the election [ 1 ]. Pakistan’s ministry of Defense posted a tweet fiercely condemning Israel after coming to believe that Israel had threatened Pakistan with nuclear weapons, which was later found to be false [ 2 ]. Recently, the spread of the absurd rumor that COVID-19 propagates through 5G base stations in the UK caused many people to become upset and resulted in a base station being set on fire [ 3 ].

Such fake news phenomenon has been rapidly evolving with the emergence of social media [ 4 , 5 ]. Fake news can be quickly shared by friends, followers, or even strangers within only a few seconds. Repeating a series of these processes could lead the public to form the wrong collective intelligence [ 6 ]. This could further develop into diverse social problems (i.e., setting a base station on fire because of rumors). In addition, some people believe and propagate fake news due to their personal norms, regardless of the factuality of the content [ 7 ]. Research in social science has suggested that cognitive bias (e.g., confirmation bias, bandwagon effect, and choice-supportive bias) [ 8 ] is one of the most pivotal factors in making irrational decisions in terms of the both creation and consumption of fake news [ 9 , 10 ]. Cognitive bias greatly contributes to the formation and enhancement of the echo chamber [ 11 ], meaning that news consumers share and consume information only in the direction of strengthening their beliefs [ 12 ].

Research using computational techniques (e.g., machine or deep learning) has been actively conducted for the past decade to investigate the current state of fake news and detect it effectively [ 13 ]. In particular, research into text-based feature selection and the development of detection models has been very actively and extensively conducted [ 14 – 17 ]. Research has been also active in the collection of fake news datasets [ 18 , 19 ] and fact-checking methodologies for model development [ 20 – 22 ]. Recently, Deepfake, which can manipulate images or videos through deep learning technology, has been used to create fake news images or videos, significantly increasing social concerns [ 23 ], and a growing body of research is being conducted to find ways of mitigating such concerns [ 24 – 26 ]. In addition, some research on system development (i.e., a game to increase awareness of the negative aspects of fake news) has been conducted to educate the public to avoid and prevent them from the situation where they could fall into the echo chamber, misunderstandings, wrong decision-making, blind belief, and propagating fake news [ 27 – 29 ].

While the creation and consumption of fake news are clearly different behaviors, due to the characteristics of the online environment (e.g., information can be easily created, shared, and consumed by anyone at anytime from anywhere), the boundaries between fake news creators and consumers have started to become blurred. Depending on the situation, people can quickly change their roles from fake news consumers to creators, or vice versa (with or without their intention). Furthermore, news creation and consumption are the most fundamental aspects that form the relationship between news and people. However, a significant amount of fake news research has positioned in news creation while considerably less research focus has been placed in news consumption (see Figs 1 & 2 ). This suggests that we must consider fake news as a comprehensive aspect of news consumption and creation .

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

https://doi.org/10.1371/journal.pone.0260080.g001

The papers were published in IEEE, ACM, ELSEVIER, arXiv, Wiley, APA from 2010 to 2020 classified by publisher, main category, sub category, and evaluation method (left to right).

https://doi.org/10.1371/journal.pone.0260080.g002

In this paper, we looked into fake news research through the lens of news creation and consumption ( Fig 3 ). Our survey results offer different yet salient insights on fake news research compared with other survey papers (e.g., [ 13 , 30 , 31 ]), which primarily focus on fake news creation. The main contributions of our survey are as follows:

• We investigate trends in fake news research from 2010 to 2020 and confirm a need for applying a comprehensive perspective to fake news phenomenon.
• We present fake news research through the lens of news creation and consumption with external and internal factors.
• We examine key findings with a mental model approach, which highlights individuals’ differences in information understandings, expectations, or consumption.
• We summarize our review and discuss complementary roles of computer and social sciences and potential future directions for fake news research.

We investigate fake news research trend (Section 2), and examine fake news creation and consumption through the lenses of external and internal factors. We also investigate research efforts to mitigate external factors of fake news creation and consumption: (a) indicates fake news creation (Section 3), and (b) indicates fake news consumption (Section 4). “Possible moves” indicates that news consumers “possibly” create/propagate fake news without being aware of any negative impact.

https://doi.org/10.1371/journal.pone.0260080.g003

2 Fake news definition and trends

There is still no definition of fake news that can encompass false news and various types of disinformation (e.g., satire, fabricated content) and can reach a social consensus [ 30 ]. The definition continues to change over time and may vary depending on the research focus. Some research has defined fake news as false news based on the intention and factuality of the information [ 4 , 15 , 32 – 36 ]. For example, Allcott and Gentzkow [ 4 ] defined fake news as “news articles that are intentionally and verifiably false and could mislead readers.” On the other hand, other studies have defined it as “a news article or message published and propagated through media, carrying false information regardless of the means and motives behind it” [ 13 , 37 – 43 ]. Given this definition, fake news refers to false information that causes an individual to be deceived or doubt the truth, and fake news can only be useful if it actually deceives or confuses consumers. Zhou and Zafarani [ 31 ] proposed a broad definition (“Fake news is false news.”) that encompasses false online content and a narrow definition (“Fake news is intentionally and verifiably false news published by a news outlet.”). The narrow definition is valid from the fake news creation perspective. However, given that fake news creators and consumers are now interchangeable (e.g., news consumers also play a role of gatekeeper for fake news propagation), it has become important to understand and investigate the fake news through consumption perspectives. Thus, in this paper, we use the broad definition of fake news.

Our research motivation for considering news creation and consumption in fake news research was based on the trend analysis. We collected 2,277 fake news-related literature using four keywords (i.e., fake news, false information, misinformation, rumor) to identify longitudinal trends of fake news research from 2010 to 2020. The data collection was conducted from July to September 2020. The criteria of data collection was whether any of these keywords exists in the title or abstract. To reflect diverse research backgrounds/domains, we considered six primary publishers (ACM, IEEE, arXiv, APA, ELSEVIER, and Wiley). The number of papers collected for each publisher is as follows: 852 IEEE (37%), 639 ACM (28%), 463 ELSEVIER (20%), 142 arXiv (7%), 141 Wiley (6%), 40 APA (2%). We excluded 59 papers that did not have the abstract and used 2,218 papers for the analysis. We then randomly chose 200 papers, and two coders conducted manual inspection and categorization. The inter-coder reliability was verified by the Cohen’s Kappa measurement. The scores for each main/sub-category were higher than 0.72 (min: 0.72, max: 0.95, avg: 0.85), indicating that the inter-coder reliability lies between “substantial” to “perfect” [ 44 ]. Through the coding procedure, we excluded non-English studies (n = 12) and reports on study protocol only (n = 6), and 182 papers were included in synthesis. The PRISMA flow chart depicts the number of articles identified, included, and excluded (see Fig 1 ).

The papers were categorized into two main categories: (1) creation (studies with efforts to detect fake news or mitigate spread of fake news) and (2) consumption (studies that reported the social impacts of fake news on individuals or societies and how to appropriately handle fake news). Each main category was then classified into sub-categories. Fig 4 shows the frequency of the entire literature by year and the overall trend of fake news research. It appears that the consumption perspective of fake news still has not received sufficient attention compared with the creation perspective ( Fig 4(a) ). Fake news studies have exploded since the 2016 US Presidential Election, and the trend of increase in fake news research continues. In the creation category, the majority of papers (135 out of 158; 85%) were related to the false information (e.g., fake news, rumor, clickbait, spam) detection model ( Fig 4(b) ). On the other hand, in the consumption category, much research pertains to data-driven fake news trend analysis (18 out of 42; 43%) or fake content consumption behavior (16 out of 42; 38%), including studies for media literacy education or echo chamber awareness ( Fig 4(c) ).

We collected 2,277 fake news related-papers and randomly chose and categorized 200 papers. Each marker indicates the number of fake news studies per type published in a given year. Fig 4(a) shows a research trend of news creation and consumption (main category). Fig 4(b) and 4(c) show a trend of the sub-categories of news creation and consumption. In Fig 4(b), “Miscellaneous” includes studies on stance/propaganda detection and a survey paper. In Fig 4(c), “Data-driven fake news trend analysis” mainly covers the studies reporting the influence of fake news that spread around specific political/social events (e.g., fake news in Presidential Election 2016, Rumor in Weibo after 2015 Tianjin explosions). “Conspiracy theory” refers to an unverified rumor that was passed on to the public.

https://doi.org/10.1371/journal.pone.0260080.g004

3 Fake news creation

Fake news is no longer merely propaganda spread by inflammatory politicians; it is also made for financial benefit or personal enjoyment [ 45 ]. With the development of social media platforms people often create completely false information for reasons beyond satire. Further, there is a vicious cycle of this false information being abused by politicians and agitators.

Fake news creators are indiscriminately producing fake news while considering the behavioral and psychological characteristics of today’s news consumers [ 46 ]. For instance, the sleeper effect [ 47 ] refers to a phenomenon in which the persuasion effect increases over time, even though the pedigree of information shows low reliability. In other words, after a long period of time, memories of the pedigree become poor and only the content tends to be remembered regardless of the reliability of the pedigree. Through this process, less reliable information becomes more persuasive over time. Fake news creators have effectively created and propagated fake news by targeting the public’s preference for news consumption through peripheral processing routes [ 35 , 48 ].

Peripheral routes are based on the elaboration likelihood model (ELM) [ 49 ], one of the representative psychological theories that handles persuasive messages. According to the ELM, the path of persuasive message processing can be divided into the central and the peripheral routes depending on the level of involvement. On one hand, if the message recipient puts a great deal of cognitive effort into processing, the central path is chosen. On the other hand, if the process of the message is limited due to personal characteristics or distractions, the peripheral route is chosen. Through a peripheral route, a decision is made based on other secondary cues (e.g., speakers, comments) rather than the logic or strength of the argument.

Wang et al. [ 50 ] demonstrated that most of the links shared or mentioned in social media have never even been clicked. This implies that many people perceive and process information in only fragmentary way, such as via news headlines and the people sharing news, rather than considering the logical flow of news content.

In this section, we closely examined each of the external and internal factors affecting fake news creation, as well as the research efforts carried out to mitigate the negative results based on the fake news creation perspective.

3.1 External factors: Fake news creation facilitators

We identified two external factors that facilitate fake news creation and propagation: (1) the unification of news creation, consumption, and distribution, (2) the misuse of AI technology, and (3) the use of social media as a news platform (see Fig 5 ).

We identify two external factors—The unification of news and the misuse of AI technology—That facilitate fake news creation.

https://doi.org/10.1371/journal.pone.0260080.g005

3.1.1 The unification of news creation, consumption, and distribution.

The public’s perception of news and the major media of news consumption has gradually changed. The public no longer passively consumes news exclusively through traditional news organizations with specific formats (e.g., the inverted pyramid style, verified sources) nor view those news simply as a medium for information acquisition. The public’s active news consumption behaviors began in earnest with the advent of citizen journalism by implementing journalistic behavior based on citizen participation [ 51 ] and became commonplace with the emergence of social media. As a result, the public began to prefer interactive media, in which new information could be acquired, their opinions can be offered, and they can discuss the news with other news consumers. This environment has motivated the public to make content about their beliefs and deliver the content to many people as “news.” For example, a recent police crackdown video posted in social media quickly spread around the world that influenced protesters and civic movements. Then, it was reported later by the mainstream media [ 52 ].

The boundaries between professional journalists and amateurs, as well as between news consumers and creators, are disappearing. This has led to a potential increase in deceptive communications, making news consumers suspicious and misinterpreted the reality. Online platforms (e.g., YouTube, Facebook) that allow users to freely produce and distribute content have been growing significantly. As a result, fake news content can be used to attract secondary income (e.g., multinational enterprises’ advertising fees), which contributes to accelerating fake news creation and propagation. An environment in which the public can only consume news that suits their preferences and personal cognitive biases has made it much easier for fake news creators to achieve their specific purposes (e.g., supporting a certain political party or a candidate they favor).

3.1.2 The misuse of AI technology.

The development of AI technology has made it easier to develop and utilize tools for creating fake news, and many studies have confirmed the impact of these technologies— (1) social bots, (2) trolls, and (3) fake media —on social networks and democracy over the past decade.

3.1.2.1 Social bots . Shao et al. [ 53 ] analyzed the pattern of fake news spread and confirmed that social bots play a significant role in fake news propagation and social bot-based automated accounts were largely affected by the initial stage of spreading fake news. In general, it is uneasy for the public to determine whether such accounts are people or bots. In addition, social bots are not illegal tools and many companies legally purchase them as a part of marketing, thus it is not easy to curb the use of social bots systematically.

3.1.2.2 Trolls . The term “trolls” refers to people who deliberately cause conflict or division by uploading inflammatory, provocative content or unrelated posts to online communities. They work with the aim of stimulating people’s feelings or beliefs and hindering mature discussions. For example, the Russian troll army has been active in social media to advance its political agenda and cause social turmoil in the US [ 54 ]. Zannettou et al. [ 55 ] confirmed how effectively the Russian troll army has been spreading fake news URLs on Twitter and its significant impact on making other Twitter users believe misleading information.

3.1.2.3 Fake media . It is now possible to manipulate or reproduce content in 2D or even 3D through AI technology. In particular, the advent of fake news using Deepfake technology (combining various images on an original video and generating a different video) has raised another major social concern that had not been imagined before. Due to the popularity of image or video sharing on social media, such media types have become the dominant form of news consumption, and the Deepfake technology itself is becoming more advanced and applied to images and videos in a variety of domains. We witnessed a video clip of former US President Barack Obama criticizing Donald Trump, which was manipulated by the US online media company BuzzFeed to highlight the influence and danger of Deepfake, causing substantial social confusion [ 56 ].

3.2 Internal factors: Fake news creation purposes

We identified three main purposes for fake news creation— (1) ideological purposes, (2) monetary purposes, and (3) fear/panic reduction .

3.2.1 Ideological purpose.

Fake news has been created and propagated for political purposes by individuals or groups that positively affect the parties or candidates they support or undermine those who are not on the same side. Fake news with this political purpose has shown to negatively influence people and society. For instance, Russia created a fake Facebook account that caused many political disputes and enhanced polarization, affecting the 2016 US Presidential Election [ 57 ]. As polarization has intensified, there has also been a trend in the US that “unfriending” people who have different political tendencies [ 58 ]. This has led the public to decide whether to trust the news or not regardless of its factuality and has resulted in worsening in-group biases. During the Brexit campaign in the UK, many selective news articles were exposed on Facebook, and social bots and trolls were also confirmed as being involved in creating public opinions [ 59 , 60 ].

3.2.2 Monetary purpose.

Financial benefit is another strong motivation for many fake news creators [ 34 , 61 ]. Fake news websites usually reach the public through social media and make profits through posted advertisements. The majority of fake websites are focused on earning advertising revenue by spreading fake news that would attract readers’ attention, rather than political goals. For example, during the 2016 US Presidential Election in Macedonia, young people in their 10s and 20s used content from some extremely right-leaning blogs in the US to mass-produce fake news, earning huge advertising revenues [ 62 ]. This is also why fake news creators use provocative titles, such as clickbait headlines, to induce clicks and attempt to produce as many fake news articles as possible.

3.2.3 Fear and panic reduction.

In general, when epidemics become more common around the world, rumors of absurd and false medical tips spread rapidly in social media. When there is a lack of verified information, people feel great anxious and afraid and easily believe such tips, regardless of whether they are true [ 63 , 64 ]. The term infodemic , which first appeared during the 2003 SARS pandemics, describes this phenomenon [ 65 ]. Regarding COVID-19, health authorities have recently announced that preventing the creation and propagation of fake news about the virus is as important as alleviating the contagious power of COVID-19 [ 66 , 67 ]. The spread of fake news due to the absence of verified information has become more common regarding health-related social issues (e.g., infectious diseases), natural disasters, etc. For example, people with disorders affecting cognition (e.g., neurodegenerative disorder) are tend to easily believe unverified medical news [ 68 – 70 ]. Robledo and Jankovic [ 68 ] confirmed that many fake or exaggerated medical journals are misleading people with Parkinson’s disease by giving false hopes and unfounded fake articles. Another example is a rumor that climate activists set fire to raise awareness of climate change quickly spread as fake news [ 71 ], when a wildfire broke out in Australia in 2019. As a result, people became suspicious and tended to believe that the causes of climate change (e.g., global warming) may not be related to humans, despite scientific evidence and research data.

3.3 Fake news detection and prevention

The main purpose of fake news creation is to make people confused or deceived regardless of topic, social atmosphere, or timing. Due to this purpose, it appears that fake news tends to have similar frames and structural patterns. Many studies have attempted to mitigate the spread of fake news based on these identifiable patterns. In particular, research on developing computational models that detect fake information (text/images/videos), based on machine or deep learning techniques has been actively conducted, as summarized in Table 1 . Other modeling studies include the credibility of weblogs [ 84 , 85 ], communication quality [ 88 ], susceptibility level [ 90 ], and political stance [ 86 , 87 ]. The table was intended to characterize a research scope and direction of the development of fake information creation (e.g., the features employed in each model development), not to present an exhaustive list.

https://doi.org/10.1371/journal.pone.0260080.t001

3.3.1 Fake text information detection.

Research has considered many text-based features, such as structural (e.g., website URLs and headlines with all capital letters or exclamations) and linguistic information (e.g., grammar, spelling, and punctuation errors) about the news. Research has also considered the sentiments of news articles, the frequency of the words used, user information, and who left comments on the news articles, and social network information among users (who were connected based on activities of commenting, replying, liking or following) were used as key features for model development. These text-based models have been developed for not only fake news articles but also other types of fake information, such as clickbaits, fake reviews, spams, and spammers. Many of the models developed in this context performed a binary classification that distinguished between fake and non-fake articles, with the accuracy of such models ranging from 86% to 93%. Mainstream news articles were used to build most models, and some studies used articles on social media, such as Twitter [ 15 , 17 ]. Some studies developed fake news detection models by extracting features from images, as well as text, in news articles [ 16 , 17 , 75 ].

3.3.2 Fake visual media detection.

The generative adversary network (GAN) is an unsupervised learning method that estimates the probability distribution of original data and allows an artificial neural network to produce similar distributions [ 109 ]. With the advancement of GAN, it has become possible to transform faces in images into those of others. However, photos of famous celebrities have been misused (e.g., being distorted into pornographic videos), increasing concerns about the possible misuse of such technology [ 110 ] (e.g., creating rumors about a certain political candidate). To mitigate this, research has been conducted to develop detection models for fake images. Most studies developed binary classification models (fake image or not), and the accuracy of fake image detection models was high, ranging from 81% to 97%. However, challenges still exist. Unlike fake news detection models that employ fact-checking websites or mainstream news as data verification or ground-truth, fake image detection models were developed using the same or slightly modified image datasets (e.g., CelebA [ 97 ], FFHQ [ 99 ]), asking for the collection and preparation of a large amount of highly diverse data.

4 Fake news consumption

4.1 external factors: fake news consumption circumstances.

The implicit social contract between civil society and the media has gradually disintegrated in modern society, and accordingly, citizens’ trust in the media began to decline [ 111 ]. In addition, the growing number of digital media platforms has changed people’s news consumption environment. This change has increased the diversity of news content and the autonomy of information creation and sharing. At the same time, however, it blurred the line between traditional mainstream media news and fake news in the Internet environment, contributing to polarization.

Here, we identified three external factors that have forced the public to encounter fake news: (1) the decline of trust in the mainstream media, (2) a high-choice media environment, and (3) the use of social media as a news platform .

4.1.1 Fall of mainstream media trust.

Misinformation and unverified or biased reports have gradually undermined the credibility of the mainstream media. According to the 2019 American mass media trust survey conducted by Gallup, only 13% of Americans said they trusted traditional mainstream media: newspapers or TV news [ 112 ]. The decline in traditional media trust is not only a problem for the US, but also a common concern in Europe and Asia [ 113 – 115 ].

4.1.2 High-choice media environment.

Over the past decade, news consumption channels have been radically diversified, and the mainstream has shifted from broadcasting and print media to mobile and social media environments. Despite the diversity of news consumption channels, personalized preferences and repetitive patterns have led people to be exposed to limited information and continue to consume such information increasingly [ 116 ]. This selective news consumption attitude has enhanced the polarization of the public in many multi-media environments [ 117 ]. In addition, the commercialization of digital platforms have created an environment in which cognitive bias can be easily strengthened. In other words, a digital platform based on recommended algorithms has the convenience of providing similar content continuously after a given type of content is consumed. As a result, it may be easy for users to fall into the echo chamber because they only access recommended content. A survey of 1,000 YouTube videos found that more than two-thirds of the videos contained content in favor of a particular candidate [ 118 ].

News consumption in social media does not simply mean the delivery of messages from creators to consumers. The multi-directionality of social media has blurred the boundaries between information creators and consumers. In other words, users are already interacting with one another in various fashions, and when a new interaction type emerges and is supported by the platform, users will display other types of new interactions, which will also influence ways of consuming news information.

4.1.3 Use of social media as news platform.

Here we focus on the most widely used social media platforms—YouTube, Facebook, and Twitter—where each has characteristics of encouraging limited news consumption.

First, YouTube is the most unidirectional of social media. Many YouTube creators tend to convey arguments in a strong, definitive tone through their videos, and these content characteristics make viewers judge the objectivity of the information via non-verbal elements (e.g., speaker, thumbnail, title, comments) rather than facts. Furthermore, many comments often support the content of the video, which may increase the chances of viewers accepting somewhat biased information. In addition, a YouTube video recommendation algorithm causes users who watch certain news to continuously be exposed to other news containing the same or similar information. This behavior and direction on the part of isolated content consumption could undermine the viewer’s media literacy, and is likely to create a screening effect that blocks the user’s eyes and ears.

Second, Facebook is somewhat invisible regarding the details of news articles because this platform ostensibly shows only the title, the number of likes, and the comments of the posts. Often, users have to click on the article and go to the URL to read the article. This structure and consumptive content orientation on the part of Facebook presents obstacles that prevent users from checking the details of their posts. As a result, users have become likely to make limited and biased judgments and perceive content through provocative headlines and comments.

Third, the largest feature of Twitter is anonymity because Twitter asks users to make their own pseudonyms [ 119 ]. Twitter has a limited number of letters to upload, and compared to other platforms, users can produce and spread indiscriminate information anonymously and do not know who is behind the anonymity [ 120 , 121 ]. On the other hand, many accounts on Facebook operate under real names and generally share information with others who are friends or followers. Information creators are not held accountable for anonymous information.

4.2 Internal factors: Cognitive mechanism

Due to the characteristics of the Internet and social media, people are accustomed to consuming information quickly, such as reading only news headlines and checking photos in news articles. This type of news consumption practice could lead people to consider news information mostly based on their beliefs or values. This practice can make it easier for people to fall into an echo chamber and further social confusion. We identified two internal factors affecting fake news consumption: (1) cognitive biases and (2) personal traits (see Fig 6 ).

https://doi.org/10.1371/journal.pone.0260080.g006

4.2.1 Cognitive biases.

Cognitive bias is an observer effect that is broadly recognized in cognitive science and includes basic statistical and memory errors [ 8 ]. However, this bias may vary depending on what factors are most important to affect individual judgments and choices. We identified five cognitive biases that affect fake news consumption: confirmation bias, in-group bias, choice-supportive bias, cognitive dissonance, and primacy effect.

Confirmation bias relates to a human tendency to seek out information in line with personal thoughts or beliefs, as well as to ignore information that goes against such beliefs. This stems from the human desire to be reaffirmed, rather than accept denials of one’s opinion or hypothesis. If the process of confirmation bias is repeated, a more solid belief is gradually formed, and the belief remains unchanged even after encountering logical and objective counterexamples. Evaluating information with an objective attitude is essential to properly investigating any social phenomenon. However, confirmation bias significantly hinders this. Kunda [ 122 ] discussed experiments that investigated the cognitive processes as a function of accuracy goals and directional goals. Her analysis demonstrated that people use different cognitive processes to achieve the two different goals. For those who pursue accuracy goals (reaching a “right conclusion”), information is used as a tool to determine whether they are right or not [ 123 ], and for those with directional goals (reaching a desirable conclusion), information is used as a tool to justify their claims. Thus, biased information processing is more frequently observed by people with directional goals [ 124 ].

People with directional goals have a desire to reach the conclusion they want. The more we emphasize the seriousness and omnipresence of fake news, the less people with directional goals can identify fake news. Moreover, their confirmation bias through social media could result in an echo chamber, triggering a differentiation of public opinion in the media. The algorithm of the media platform further strengthens the tendency of biased information consumption (e.g., filter bubble).

In-group bias is a phenomenon in which an individual favors a group that he or she belongs to. The causes of in-group bias are two [ 125 ]. One is a categorization process, which exaggerates the similarities between members within one category (the internal group) and differences with others (the external groups). Consequently, positive reactions towards the internal group and negative reactions (e.g., hostility) towards the external group are both increased. The other reason is self-respect based on social identity theory. To positively evaluate the internal group, a member tends to perceive that other group members are similar to himself or herself.

In-group bias has a significant impact on fake news consumption because of radical changes in the media environment [ 126 ]. The public recognizes and forms groups based on issues through social media. The emotions and intentions of such groups of people online can be easily transferred or developed into offline activities, such as demonstrations and rallies. Information exchanges within such internal groups proceeds similarly to the situation with confirmation bias. If confirmation bias is keeping to one’s beliefs, in-group bias equates the beliefs of my group with my beliefs.

Choice-supportive bias refers to an individual’s tendency to justify his or her decision by highlighting the evidence that he or she did not consider in making the decision [ 127 ]. For instance, people sometimes have no particular purpose when they purchase a certain brand of products or service, or support a particular politician or political party. They emphasize that their choices at the time were right and inevitable. They also tend to focus more on positive aspects than negative effects or consequences to justify their choice. However, these positive aspects can be distorted because they are mainly based on memory. Thus, choice-supportive bias, can be regarded as the cognitive errors caused by memory distortion.

The behavioral condition of choice-supportive bias is used to justify oneself, which usually occurs in the context of external factors (e.g., maintaining social status or relationships) [ 7 ]. For example, if people express a certain political opinion within a social group, people may seek information with which to justify the opinion and minimize its flaws. In this procedure, people may accept fake news as a supporting source for their opinions.

Cognitive dissonance was based on the notion that some psychological tension would occur when an individual had two perceptions that were inconsistent [ 128 ]. Humans have a desire to identify and resolve the psychological tension that occurs when a cognitive dissonance is established. Regarding fake news consumption, people easily accept fake news if it is aligned with their beliefs or faith. However, if such news is seen as working against their beliefs or faith, people define even real news as fake and consume biased information in order to avoid cognitive dissonance. This is quite similar to cognitive bias. Selective exposure to biased information intensifies its extent and impact in social media. In these circumstances, an individual’s cognitive state is likely to be formed by information from unclear sources, which can be seen as a negative state of perception. In that case, information consumers selectively consume only information that can be in harmony with negative perceptions.

Primacy effect means that information presented previously will have a stronger effect on the memory and decision-making than information presented later [ 129 ]. The “interference theory [ 130 ]” is often referred to as a theoretical basis for supporting the primacy effect, which highlights the fact that the impression formed by the information presented earlier influences subsequent judgments and the process of forming the next impression.

The significance of the primary effect for fake news consumption is that it can be a starting point for biased cognitive processes. If an individual first encounters an issue in fake news and does not go through a critical thinking process about that information, he or she may form false attitudes regarding the issue [ 131 , 132 ]. Fake news is a complex combination of facts and fiction, making it difficult for information consumers to correctly judge whether the news is right or wrong. These cognitive biases induce the selective collection of information that feels more valid for news consumers, rather than information that is really valid.

4.2.2 Personal traits.

We two aspects of personal characteristics or traits can influence one’s behaviors in terms of news consumption: susceptibility and personality.

4.2.2.1 Susceptibility . The most prominent feature of social media is that consumers can be also creators, and the boundaries between the creators and consumers of information become unclear. New media literacy (i.e., the ability to critically and suitably consume messages in a variety of digital media channels, such as social media) can have a significant impact on the degree of consumption and dissemination of fake news [ 133 , 134 ]. In other words, the higher new media literacy is, the higher the probability that an individual is likely to take a critical standpoint toward fake news. Also, the susceptibility level of fake news is related to one’s selective news consumption behaviors. Bessi et al. [ 35 ] studied misinformation on Facebook and found that users who frequently interact with alternative media tend to interact with intentionally false claims more often.

Personality is an individual’s traits or behavior style. Many scholars have agreed that the personality can be largely divided into five categories (Big Five)—extraversion, agreeableness, neuroticism, openness, and conscientiousness [ 135 , 136 ]—and used them to understand the relationship between personality and news consumption.

Extroversion is related to active information use. Previous studies have confirmed that extroverts tend to use social media and that their main purpose of use is to acquire information [ 137 ] and better determine the factuality of news on social media [ 138 ]. Furthermore, people with high agreeableness, which refers to how friendly, warm, and tactful, tend to trust real news than fake news [ 138 ]. Neuroticism refers to a broad personality trait dimension representing the degree to which a person experiences the world as distressing, threatening, and unsafe. People with high neuroticism usually show negative emotions or information sharing behavior [ 139 ]. Neuroticism is positively related to fake news consumption [ 138 ]. Openness refers to the degree of enjoying new experiences. High openness is associated with high curiosity and engagement in learning [ 140 ], which enhances critical thinking ability and decreases negative effects of fake news consumption [ 138 , 141 ]. Conscientiousness refers to a person’s work ethic, being orderly, and thoroughness [ 142 ]. People with high conscientiousness tend to regard social media use as distraction from their tasks [ 143 – 145 ].

4.3 Fake news awareness and prevention

4.3.1 decision-making support tools..

News on social media does not go through the verification process, because of its high degree of freedom to create, share, and access information. The study reported that most citizens in advanced countries will have more fake information than real information in 2022 [ 146 ]. This indicates that potential personal and social damage from fake news may increase. Paradoxically, many countries that suffer from fake news problems strongly guarantee the freedom of expression under their constitutions; thus, it would be very difficult to block all possible production and distribution of fake news sources through laws and regulations. In this respect, it would be necessary to put in place not only technical efforts to detect and prevent the production and dissemination of fake news but also social efforts to make news consumers aware of the characteristics of online fake information.

Inoculation theory highlights that human attitudes and beliefs can form psychological resistance by being properly exposed to arguments against belief in advance. To have the ability to strongly protest an argument, it is necessary to expose and refute the same sort of content with weak arguments first. Doris-Down et al. [ 147 ] asked people who were from different political backgrounds to communicate directly through mobile apps and investigated whether these methods alleviated their echo-chamberness. As a result, the participants made changes, such as realizing that they had a lot in common with people who had conflicting political backgrounds and that what they thought was different was actually trivial. Karduni et al. [ 148 ] provided comprehensive information (e.g., connections among news accounts and a summary of the location entities) to study participants through the developed visual analytic system and examined how they accepted fake news. Another study was conducted to confirm how people determine the veracity of news by establishing a system similar to social media and analyzing the eye tracking of the study participants while reading fake news articles [ 28 ].

Some research has applied the inoculation theory to gamification. A “Bad News” game was designed to proactively warn people and expose them to a certain amount of false information through interactions with the gamified system [ 29 , 149 ]. The results confirmed the high effectiveness of inoculation through the game and highlighted the need to educate people about how to respond appropriately to misinformation through computer systems and games [ 29 ].

4.3.2 Fake information propagation analysis.

Fake information tends to show a certain pattern in terms of consumption and propagation, and many studies have attempted to identify the propagation patterns of fake information (e.g., the count of unique users, the depth of a network) [ 150 – 153 ].

4.3.2.1 Psychological characteristics . The theoretical foundation of research intended to examine the diffusion patterns of fake news lies in psychology [ 154 , 155 ] because psychological theories explain why and how people react to fake news. For instance, a news consumer who comes across fake news will first have doubts, judge the news against his background knowledge, and want to clarify the sources in the news. This series of processes ends when sufficient evidence is collected. Then the news consumer ends in accepting, ignoring, or suspecting the news. The psychological elements that can be defined in this process are doubts, negatives, conjectures, and skepticism [ 156 ].

4.3.2.2 Temporal characteristics . Fake news exhibits different propagation patterns from real news. The propagation of real news tends to slowly decrease over time after a single peak in the public’s interest, whereas fake news does not have a fixed timing for peak consumption, and a number of peaks appear in many cases [ 157 ]. Tambuscio et al. [ 151 ] proved that the pattern of the spread of rumors is similar to the existing epidemic model [ 158 ]. Their empirical observations confirmed that the same fake news reappears periodically and infects news consumers. For example, rumors that include the malicious political message that “Obama is a Muslim” are still being spread a decade later [ 159 ]. This pattern of proliferation and consumption shows that fake news may be consumed for a certain purpose.

5 A mental-model approach

We have examined news consumers’ susceptibility to fake news due to internal and external factors, including personal traits, cognitive biases, and the contexts. Beyond an investigation on the factor level, we seek to understand people’s susceptibility to misinformation by considering people’s internal representations and external environments holistically [ 5 ]. Specifically, we propose to comprehend people’s mental models of fake news. In this section, we first briefly introduce mental models and discuss their connection to misinformation. Then, we discuss the potential contribution of using a mental-model approach to the field of misinformation.

5.1 Mental models

A mental model is an internal representation or simulation that people carry in their minds of how the world works [ 160 , 161 ]. Typically, mental models are constructed in people’s working memory, in which information from long-term memory and the environments are combined [ 162 ]. They also indicate that individuals represent complex phenomena with somewhat abstraction based on their own experiences and understanding of the contexts. People rely on mental models to understand and predict their interactions with environments, artifacts and computing systems, as well as other individuals [ 163 , 164 ]. Generally, individuals’ ability to represent the continually changing environments is limited and unique. Thus, mental models tend to be functional and dynamic but not necessarily accurate or complete [ 163 , 165 ]. Mental models also differ between various groups and in particular between experts and novices [ 164 , 166 ].

5.2 Mental models and misinformation

Mental models have been proposed to understand human behaviors in spatial navigation [ 167 ], learning [ 168 , 169 ], deductive reasoning [ 170 ], mental presentations of real or imagined situations [ 171 ], risk communication [ 172 ], and usable cybersecurity and privacy [ 166 , 173 , 174 ]. People use mental models to facilitate their comprehension, judgment, and actions, and can be the basis of individual behaviors. In particular, the connection between a mental-model approach and misinformation has been revealed in risk communication regarding vaccines [ 175 , 176 ]. For example, Downs et al. [ 176 ] interviewed 30 parents from three US cities to understand their mental models about vaccination for their children aged 18 to 23 months. The results revealed two mental models about vaccination: (1) heath oriented : parents who focused on health-oriented topics trusted anecdotal communication more than statistical arguments; and (2) risk oriented : parents with some knowledge about vaccine mechanisms trusted communication with statistical arguments more than anecdotal information. Also, the authors found that many parents, even those favorable to vaccination, can be confused by ongoing debate, suggesting somewhat incompleteness of their mental models.

5.3 Potential contributions of a mental-model approach

Recognizing and dealing with the plurality of news consumers’ perception, cognition and actions is currently considered as key aspects of misinformation research. Thus, a mental model approach could significantly improve our understanding of people’s susceptibility to misinformation, as well as inform the development of mechanisms to mitigate misinformation.

One possible direction is to investigate the demographic differences in the context of mental models. As more Americans have adopted social media, the social media users have become more representative for the population. Usage by older adults has increased in recent years, with the use rate of about 12% in 2012 to about 35% in 2016 ( https://www.pewresearch.org/internet/fact-sheet/social-media/ ). Guess et al. (2019) analyzed participants’ profiles and their sharing activity on Facebook during the 2016 US Presidential campaign. A strong age effect was revealed. While controlled the effects of ideology and education, their results showed that Facebook users who are over 65 years old were associated with sharing nearly seven times as many articles from fake news domains on Facebook as those who are between 18–29 years old, or about 2.3 times as many as those in the age between 45 to 65.

Besides older adults, college students were shown more susceptibility to misinformation [ 177 ]. We can identify which mental models a particular age group ascribes to, and compare the incompleteness or incorrectness of the mental models by age. On the other hand, such comparison might be informative to design general mechanisms to mitigate misinformation independent of the different concrete mental models possessed by different types of users.

Users’ actions and decisions are directed by their mental models. We can also explore news consumers’ mental models and discover unanticipated and potentially risky human system interactions, which will inform the development and design of user interactions and education endeavors to mitigate misinformation.

A mental-model approach supplies an important, and as yet unconsidered, dimension to fake news research. To date, research on people’s susceptibility to fake news in social media has lagged behind research on computational aspect research on fake news. Scholars have not considered issues of news consumers’ susceptibility across the spectrum of their internal representations and external environments. An investigation from the mental model’s perspective is a step toward addressing such need.

6 Discussion and future work

In this section, we highlight the importance of balancing research efforts on fake news creation and consumption and discuss potential future directions of fake news research.

6.1 Leveraging insights of social science to model development

Developing fake news detection models has achieved great performance. Feature groups used in the model are diverse including linguistics, vision, sentiment, topic, user, and network, and many models used multiple groups to increase the performance. By using datasets with different size and characteristics, research has demonstrated the effectiveness of the models through a comparison analysis. However, much research has considered and used the features that are easily quantifiable, and many of them tend to have unclear justification or rationale of being used in modeling. For example, what is the relationship between the use of question (?), exclamation (!), or quotation marks (“…”) and fake news?, what does it mean by a longer description relates to news trustworthiness?. There are also many important aspects that can be used as additional features for modeling and have not yet found a way to be quantified. For example, journalistic styles are important characteristics that determine a level of information credibility [ 156 ], but it is challenging to accurately and reliably quantified them. There are many intentions (e.g., ideological standpoint, financial gain, panic creation) that authors may implicitly or explicitly display in the post but measuring them is uneasy and not straightforward. Social science research can play a role in here coming up with a valid research methodology to measure such subjective perceptions or notions considering various types and characteristics of them depending on a context or environment. Some research efforts in this research direction include quantifying salient factors of people’s decision-making identified in social science research and demonstrating the effectiveness of using the factors in improving model performance and interpreting model results [ 70 ]. Yet more research that applies socio-technical aspects in model development and application would be needed to better study complex characteristics of fake news.

6.1.1 Future direction.

Insights from social science may help develop transparent and applicable fake news detection models. Such socio-technical models may allow news consumers to have a better understanding of fake news detection results and its application as well as to take more appropriate actions to control fake news phenomenon.

6.2 Lack of research on fake news consumption

Regarding fake news consumption, we confirmed that only few studies involve the development of web- or mobile-based technology systems to help consumers aware possible dangers of fake news. Those studies [ 28 , 29 , 147 , 148 ] tried to demonstrate the feasibility of developed self-awareness systems through user studies. However, due to the limited number of study participants (min: 11, max: 60) and their lack of demographic diversity (i.e., recruited only college students of one school, the psychology research pool at the authors’ institution), the generalization and applicability of these systems are still questionable. On the other hand, research that involves the development of fake news detection models or network analysis to identify the pattern of fake news propagation has been relatively active. These results can be used to identify people (or entities) who intentionally create malicious fake content; however, it is still challenging to restrict people who originally had not shown any behaviors or indications of sharing or creating fake information but later manipulated real news to fake or disseminated fake news with their malicious intention or cognitive biases.

In other words, although fake news detection models have shown great, promising performance, the influence of the models may be exerted in limited cases. This is because fake news detection models heavily rely on the data that were labeled as fake by other fact-checking institutions or sites. If someone manipulates the news that were not covered by fact-checking, the format or characteristics of the manipulated news may be different from those (i.e., conventional features) that are identified and managed in the detection model. Such differences may not be captured by the model. Therefore, to prevent fake news phenomenon more effectively, research needs to consider changes of news consumption.

6.2.1 Future direction.

It may be desirable to support people recognizing that their news consumption behaviors (e.g., like, comment, share) can have a significant ripple effect. Developing a system that tracks activities of people’s news consumption and creation, measures similarity and differences between those activities, and presents behaviors or patterns of news consumption and creation to people would be helpful.

6.3 Limited coverage of fact-checking websites and regulatory approach

Some of the well-known fact-checking websites (e.g., snopes.com, politifact.com) cover news shared mostly on the Internet and label the authenticity or deficiencies of the content (e.g., miscaptioned, legend, misattributed). However, these fact-checking websites may show limited coverage in that they are only used for those who are willing to check the veracity of certain news articles. Social media platforms have been making continuous efforts to mitigate the spread of fake news. For example, Facebook shows that content that has been falsely assessed by fact-checkers is relatively less exposed to news feeds or shows warning indicators [ 178 ]. Instagram has also changed the way that warning labels are displayed when users attempt to view the content that has been falsely assessed [ 179 ]. However, this type of an interface could lead news consumers to relying on algorithmic decision-making rather than self-judgment because these ostensible regulations (e.g., warning labels) tend to lack transparency of the decision. As we explained previously, this is related to filter bubbles. Therefore, it is important to provide a more clear and transparent communicative interface for news consumers to access and understand underlying information of the algorithm results.

6.3.1 Future direction.

It is necessary to create a news consumption circumstance that gives a wider coverage of fake news and more transparent information of algorithmic decisions on news credibility. This will help news consumers preemptively avoid fake news consumption and contribute more to preventing fake news propagation. Consumers also make more proper and accurate decisions based on their understanding of the news.

6.4 New media literacy

With the diversification of news channels, we can easily consume news. However, we are also in a media environment that asks us to self-critically verify news content (e.g., whether the news title reads like a clickbait, whether the news title and content are related), which in reality is hard to be done. Moreover, in social media, news consumers can be news creators or reproducers. During this process, news information could be changed based on a consumer’s beliefs or interests. A problem here is that people may not know how to verify news content or not be aware of whether the information could be distorted or biased. As the news consumer environment changes rapidly and faces modern media deluge, the importance of media literacy education is high. Media literacy refers to the ability to decipher media content, but in a broad sense, to understand the principles of media operation and media content sensibly and critically, and in turn to the ability to utilize and creatively reproduce content. Being a “lazy thinker” is more susceptible to fake news than having a “partisan bias” [ 32 ]. As “screen time” (i.e., time spent looking at smartphone, computer, or television screens) has become more common, people are consuming only stimulating (e.g., sensual pleasure and excitement) information [ 180 ]. This could gradually lower one’s ability of critical, reasonable thinking, leading to making wrong judgments and actions. In France, when fake news problem became more serious, and a great amount of efforts were made to create “European Media Literacy Week” in schools [ 181 ]. The US is also making legislative efforts to add media literacy to the general education curriculum [ 182 ]. However, the acquisition of new media literacy through education may be limited to people in school (e.g., young students) and would be challenging to be expanded to wider populations. Thus, there is also a need for supplementary tools and research efforts to support more people to critically interpret and appropriately consume news.

In addition, more critical social attention is needed because visual content (e.g., images, videos), which had been naturally accepted as facts, can be easily manipulated in a malicious fashion and looked very natural. We have seen that people prefer to watch YouTube videos for news consumption rather than reading news articles. This visual content makes it relatively easy for news consumers to trust the content compared to text-based information and makes it easier to obtain information simply by playing the video. Since visual content will become a more dominant medium in future news consumption, educating and inoculating news consumers about potential threats of fake information in such news media would be important. More attention and research are needed on the technology supporting fake visual content awareness.

6.4.1 Future direction.

Research in both computer science and social science should find ways (e.g., developing a game-based education system or curriculum) to help news consumers aware of their practice of news consumption and maintain right news consumption behaviors.

7 Conclusion

We presented a comprehensive summary of fake news research through the lenses of news creation and consumption. The trends analysis indicated a growing increase in fake news research and a great amount of research focus on news creation compared to news consumption. By looking into internal and external factors, we unpacked the characteristics of fake news creation and consumption and presented the use of people’s mental models to better understand people’s susceptibility to misinformation. Based on the reviews, we suggested four future directions on fake news research—(1) a socio-technical model development using insights from social science, (2) in-depth understanding of news consumption behaviors, (3) preemptive decision-making and action support, and (4) educational, new media literacy support—as ways to reduce the gap between news creation and consumption and between computer science and social science research and to support healthy news environments.

Supporting information

S1 checklist..

https://doi.org/10.1371/journal.pone.0260080.s001

• View Article
• Google Scholar
• 2. Goldman R. Reading fake news, Pakistani minister directs nuclear threat at Israel. The New York Times . 2016;24.
• PubMed/NCBI
• 6. Lévy P, Bononno R. Collective intelligence: Mankind’s emerging world in cyberspace. Perseus Books; 1997.
• 11. Jamieson KH, Cappella JN. Echo chamber: Rush Limbaugh and the conservative media establishment. Oxford University Press; 2008.
• 14. Shu K, Cui L, Wang S, Lee D, Liu H. defend: Explainable fake news detection. In: In Proc. of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD); 2019. p. 395–405.
• 15. Ruchansky N, Seo S, Liu Y. Csi: A hybrid deep model for fake news detection. In: In Proc. of the 2017 ACM on Conference on Information and Knowledge Management (CIKM); 2017. p. 797–806.
• 16. Cui L, Wang S, Lee D. Same: sentiment-aware multi-modal embedding for detecting fake news. In: In Proc. of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM); 2019. p. 41–48.
• 17. Wang Y, Ma F, Jin Z, Yuan Y, Xun G, Jha K, et al. Eann: Event adversarial neural networks for multi-modal fake news detection. In: In Proc. of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data mining (KDD); 2018. p. 849–857.
• 18. Nørregaard J, Horne BD, Adalı S. Nela-gt-2018: A large multi-labelled news for the study of misinformation in news articles. In: In Proc. of the International AAAI Conference on Web and Social Media (ICWSM). vol. 13; 2019. p. 630–638.
• 20. Nguyen AT, Kharosekar A, Krishnan S, Krishnan S, Tate E, Wallace BC, et al. Believe it or not: Designing a human-ai partnership for mixed-initiative fact-checking. In: In Proc. of the 31st Annual ACM Symposium on User Interface Software and Technology (UIST); 2018. p. 189–199.
• 23. Brandon J. Terrifying high-tech porn: creepy’deepfake’videos are on the rise. Fox News . 2018;20.
• 24. Nguyen TT, Nguyen CM, Nguyen DT, Nguyen DT, Nahavandi S. Deep Learning for Deepfakes Creation and Detection. arXiv . 2019;1.
• 25. Rossler A, Cozzolino D, Verdoliva L, Riess C, Thies J, Nießner M. Faceforensics++: Learning to detect manipulated facial images. In: IEEE International Conference on Computer Vision (ICCV); 2019. p. 1–11.
• 26. Nirkin Y, Keller Y, Hassner T. Fsgan: Subject agnostic face swapping and reenactment. In: In Proc. of the IEEE International Conference on Computer Vision (ICCV); 2019. p. 7184–7193.
• 28. Simko J, Hanakova M, Racsko P, Tomlein M, Moro R, Bielikova M. Fake news reading on social media: an eye-tracking study. In: In Proc. of the 30th ACM Conference on Hypertext and Social Media (HT); 2019. p. 221–230.
• 35. Horne B, Adali S. This just in: Fake news packs a lot in title, uses simpler, repetitive content in text body, more similar to satire than real news. In: In Proc. of the 11th International AAAI Conference on Web and Social Media (ICWSM); 2017. p. 759–766.
• 36. Golbeck J, Mauriello M, Auxier B, Bhanushali KH, Bonk C, Bouzaghrane MA, et al. Fake news vs satire: A dataset and analysis. In: In Proc. of the 10th ACM Conference on Web Science (WebSci); 2018. p. 17–21.
• 37. Mustafaraj E, Metaxas PT. The fake news spreading plague: was it preventable? In: In Proc. of the 9th ACM Conference on Web Science (WebSci); 2017. p. 235–239.
• 40. Jin Z, Cao J, Zhang Y, Luo J. News verification by exploiting conflicting social viewpoints in microblogs. In: In Proc. of the 13th AAAI Conference on Artificial Intelligence (AAAI); 2016. p. 2972–2978.
• 41. Rubin VL, Conroy N, Chen Y, Cornwell S. Fake news or truth? using satirical cues to detect potentially misleading news. In: In Proc. of the Second Workshop on Computational Approaches to Deception Detection ; 2016. p. 7–17.
• 45. Kahneman D, Tversky A. Prospect theory: An analysis of decision under risk. In: Handbook of the fundamentals of financial decision making: Part I. World Scientific; 2013. p. 99–127.
• 46. Hanitzsch T, Wahl-Jorgensen K. Journalism studies: Developments, challenges, and future directions. The Handbook of Journalism Studies . 2020; p. 3–20.
• 48. Osatuyi B, Hughes J. A tale of two internet news platforms-real vs. fake: An elaboration likelihood model perspective. In: In Proc. of the 51st Hawaii International Conference on System Sciences (HICSS); 2018. p. 3986–3994.
• 49. Cacioppo JT, Petty RE. The elaboration likelihood model of persuasion. ACR North American Advances. 1984; p. 673–675.
• 50. Wang LX, Ramachandran A, Chaintreau A. Measuring click and share dynamics on social media: a reproducible and validated approach. In Proc of the 10th International AAAI Conference on Web and Social Media (ICWSM). 2016; p. 108–113.
• 51. Bowman S, Willis C. How audiences are shaping the future of news and information. We Media . 2003; p. 1–66.
• 52. Hill E, Tiefenthäler A, Triebert C, Jordan D, Willis H, Stein R. 8 Minutes and 46 Seconds: How George Floyd Was Killed in Police Custody; 2020. Available from: https://www.nytimes.com/2020/06/18/us/george-floyd-timing.html .
• 54. Carroll O. St Petersburg ‘troll farm’ had 90 dedicated staff working to influence US election campaign; 2017.
• 55. Zannettou S, Caulfield T, Setzer W, Sirivianos M, Stringhini G, Blackburn J. Who let the trolls out? towards understanding state-sponsored trolls. In: Proc. of the 10th ACM Conference on Web Science (WebSci); 2019. p. 353–362.
• 56. Vincent J. Watch Jordan Peele use AI to make Barack Obama deliver a PSA about fake news. The Verge . 2018;17.
• 58. Linder M. Block. Mute. Unfriend. Tensions rise on Facebook after election results. Chicago Tribune . 2016;9.
• 60. Howard PN, Kollanyi B. Bots, #StrongerIn, and #Brexit: computational propaganda during the UK-EU referendum. arXiv . 2016; p. arXiv–1606.
• 61. Kasra M, Shen C, O’Brien JF. Seeing is believing: how people fail to identify fake images on the Web. In Proc of the 2018 CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI). 2018; p. 1–6.
• 62. Kirby EJ. The city getting rich from fake news. BBC News . 2016;5.
• 63. Hu Z, Yang Z, Li Q, Zhang A, Huang Y. Infodemiological study on COVID-19 epidemic and COVID-19 infodemic. Preprints . 2020; p. 2020020380.
• 71. Knaus C. Disinformation and lies are spreading faster than Australia’s bushfires. The Guardian . 2020;11.
• 72. Karimi H, Roy P, Saba-Sadiya S, Tang J. Multi-source multi-class fake news detection. In: In Proc. of the 27th International Conference on Computational Linguistics ; 2018. p. 1546–1557.
• 73. Wang WY. “Liar, liar pants on fire”: A new benchmark dataset for fake news detection. arXiv . 2017; p. arXiv–1705.
• 74. Pérez-Rosas V, Kleinberg B, Lefevre A, Mihalcea R. Automatic Detection of Fake News. arXiv . 2017; p. arXiv–1708.
• 75. Yang Y, Zheng L, Zhang J, Cui Q, Li Z, Yu PS. TI-CNN: Convolutional Neural Networks for Fake News Detection. arXiv . 2018; p. arXiv–1806.
• 76. Kumar V, Khattar D, Gairola S, Kumar Lal Y, Varma V. Identifying clickbait: A multi-strategy approach using neural networks. In: In Proc. of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR); 2018. p. 1225–1228.
• 77. Yoon S, Park K, Shin J, Lim H, Won S, Cha M, et al. Detecting incongruity between news headline and body text via a deep hierarchical encoder. In: Proc. of the AAAI Conference on Artificial Intelligence. vol. 33; 2019. p. 791–800.
• 78. Lu Y, Zhang L, Xiao Y, Li Y. Simultaneously detecting fake reviews and review spammers using factor graph model. In: In Proc. of the 5th Annual ACM Web Science Conference (WebSci); 2013. p. 225–233.
• 79. Mukherjee A, Venkataraman V, Liu B, Glance N. What yelp fake review filter might be doing? In: In Proc. of The International AAAI Conference on Weblogs and Social Media (ICWSM); 2013. p. 409–418.
• 80. Benevenuto F, Magno G, Rodrigues T, Almeida V. Detecting spammers on twitter. In: In Proc. of the 8th Annual Collaboration , Electronic messaging , Anti-Abuse and Spam Conference (CEAS). vol. 6; 2010. p. 12.
• 81. Lee K, Caverlee J, Webb S. Uncovering social spammers: social honeypots+ machine learning. In: In Proc. of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR); 2010. p. 435–442.
• 82. Li FH, Huang M, Yang Y, Zhu X. Learning to identify review spam. In: In Proc. of the 22nd International Joint Conference on Artificial Intelligence (IJCAI); 2011. p. 2488–2493.
• 83. Wang J, Wen R, Wu C, Huang Y, Xion J. Fdgars: Fraudster detection via graph convolutional networks in online app review system. In: In Proc. of The 2019 World Wide Web Conference (WWW); 2019. p. 310–316.
• 84. Castillo C, Mendoza M, Poblete B. Information credibility on twitter. In: In Proc. of the 20th International Conference on World Wide Web (WWW); 2011. p. 675–684.
• 85. Jo Y, Kim M, Han K. How Do Humans Assess the Credibility on Web Blogs: Qualifying and Verifying Human Factors with Machine Learning. In: In Proc. of the 2019 CHI Conference on Human Factors in Computing Systems (CHI); 2019. p. 1–12.
• 86. Che X, Metaxa-Kakavouli D, Hancock JT. Fake News in the News: An Analysis of Partisan Coverage of the Fake News Phenomenon. In: In Proc. of the 21st ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW); 2018. p. 289–292.
• 87. Potthast M, Kiesel J, Reinartz K, Bevendorff J, Stein B. A Stylometric Inquiry into Hyperpartisan and Fake News. arXiv . 2017; p. arXiv–1702.
• 89. Popat K, Mukherjee S, Strötgen J, Weikum G. Credibility assessment of textual claims on the web. In: In Proc. of the 25th ACM International on Conference on Information and Knowledge Management (CIKM); 2016. p. 2173–2178.
• 90. Shen TJ, Cowell R, Gupta A, Le T, Yadav A, Lee D. How gullible are you? Predicting susceptibility to fake news. In: In Proc. of the 10th ACM Conference on Web Science (WebSci); 2019. p. 287–288.
• 91. Gupta A, Lamba H, Kumaraguru P, Joshi A. Faking sandy: characterizing and identifying fake images on twitter during hurricane sandy. In: In Proc. of the 22nd International Conference on World Wide Web ; 2013. p. 729–736.
• 92. He P, Li H, Wang H. Detection of fake images via the ensemble of deep representations from multi color spaces. In: In Proc. of the 26th IEEE International Conference on Image Processing (ICIP). IEEE; 2019. p. 2299–2303.
• 93. Sun Y, Chen Y, Wang X, Tang X. Deep learning face representation by joint identification-verification. Advances in Neural Information Processing Systems . 2014; p. 1–9.
• 94. Huh M, Liu A, Owens A, Efros AA. Fighting fake news: Image splice detection via learned self-consistency. In: In Proc. of the European Conference on Computer Vision (ECCV); 2018. p. 101–117.
• 95. Dang H, Liu F, Stehouwer J, Liu X, Jain AK. On the detection of digital face manipulation. In: In Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2020. p. 5781–5790.
• 96. Tariq S, Lee S, Kim H, Shin Y, Woo SS. Detecting both machine and human created fake face images in the wild. In Proc of the 2nd International Workshop on Multimedia Privacy and Security (MPS). 2018; p. 81–87.
• 97. Liu Z, Luo P, Wang X, Tang X. Deep learning face attributes in the wild. In: In Proc. of the IEEE International Conference on Computer Vision (ICCV); 2015. p. 3730–3738.
• 98. Wang R, Ma L, Juefei-Xu F, Xie X, Wang J, Liu Y. Fakespotter: A simple baseline for spotting ai-synthesized fake faces. arXiv . 2019; p. arXiv–1909.
• 99. Karras T, Laine S, Aila T. A style-based generator architecture for generative adversarial networks. In: In Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2019. p. 4401–4410.
• 100. Yang X, Li Y, Qi H, Lyu S. Exposing GAN-synthesized faces using landmark locations. In Proc of the ACM Workshop on Information Hiding and Multimedia Security (IH&MMSec). 2019; p. 113–118.
• 101. Zhang X, Karaman S, Chang SF. Detecting and simulating artifacts in gan fake images. In Proc of the 2019 IEEE International Workshop on Information Forensics and Security (WIFS). 2019; p. 1–6.
• 102. Amerini I, Galteri L, Caldelli R, Del Bimbo A. Deepfake video detection through optical flow based cnn. In Proc of the IEEE International Conference on Computer Vision Workshops (ICCV). 2019; p. 1205–1207.
• 103. Li Y, Lyu S. Exposing deepfake videos by detecting face warping artifacts. arXiv . 2018; p. 46–52.
• 104. Korshunov P, Marcel S. Deepfakes: a new threat to face recognition? assessment and detection. arXiv . 2018; p. arXiv–1812.
• 105. Jeon H, Bang Y, Woo SS. Faketalkerdetect: Effective and practical realistic neural talking head detection with a highly unbalanced dataset. In Proc of the IEEE International Conference on Computer Vision Workshops (ICCV). 2019; p. 1285–1287.
• 106. Chung JS, Nagrani A, Zisserman A. Voxceleb2: Deep speaker recognition. arXiv . 2018; p. arXiv–1806.
• 107. Songsri-in K, Zafeiriou S. Complement face forensic detection and localization with faciallandmarks. arXiv . 2019; p. arXiv–1910.
• 108. Ma S, Cui L, Dai D, Wei F, Sun X. Livebot: Generating live video comments based on visual and textual contexts. In Proc of the AAAI Conference on Artificial Intelligence (AAAI). 2019; p. 6810–6817.
• 109. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative adversarial nets. Advances in Neural Information Processing Systems . 2014; p. arXiv–1406.
• 110. Metz R. The number of deepfake videos online is spiking. Most are porn; 2019. Available from: https://cnn.it/3xPJRT2 .
• 111. Strömbäck J. In search of a standard: Four models of democracy and their normative implications for journalism. Journalism Studies . 2005; p. 331–345.
• 112. Brenan M. Americans’ Trust in Mass Media Edges Down to 41%; 2019. Available from: https://bit.ly/3ejl6ql .
• 114. Ladd JM. Why Americans hate the news media and how it matters. Princeton University Press; 2012.
• 116. Weisberg J. Bubble trouble: Is web personalization turning us into solipsistic twits; 2011. Available from: https://bit.ly/3xOGFqD .
• 117. Pariser E. The filter bubble: How the new personalized web is changing what we read and how we think. Penguin; 2011.
• 118. Lewis P, McCormick E. How an ex-YouTube insider investigated its secret algorithm. The Guardian . 2018;2.
• 120. Kavanaugh AL, Yang S, Li LT, Sheetz SD, Fox EA, et al. Microblogging in crisis situations: Mass protests in Iran, Tunisia, Egypt; 2011.
• 121. Mustafaraj E, Metaxas PT, Finn S, Monroy-Hernández A. Hiding in Plain Sight: A Tale of Trust and Mistrust inside a Community of Citizen Reporters. In Proc of the 6th International AAAI Conference on Weblogs and Social Media (ICWSM) . 2012; p. 250–257.
• 125. Tajfel H. Human groups and social categories: Studies in social psychology. Cup Archive ; 1981.
• 127. Correia V, Festinger L. Biased argumentation and critical thinking. Rhetoric and Cognition: Theoretical Perspectives and Persuasive Strategies . 2014; p. 89–110.
• 128. Festinger L. A theory of cognitive dissonance. Stanford University Press; 1957.
• 136. John OP, Srivastava S, et al. The Big Five trait taxonomy: History, measurement, and theoretical perspectives. Handbook of Personality: theory and research . 1999; p. 102–138.
• 138. Shu K, Wang S, Liu H. Understanding user profiles on social media for fake news detection. In: 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR). IEEE; 2018. p. 430–435.
• 142. Costa PT, McCrae RR. The NEO personality inventory. Psychological Assessment Resources; 1985.
• 146. Panetta K. Gartner top strategic predictions for 2018 and beyond; 2017. Available from: https://gtnr.it/33kuljQ .
• 147. Doris-Down A, Versee H, Gilbert E. Political blend: an application designed to bring people together based on political differences. In Proc of the 6th International Conference on Communities and Technologies (C&T). 2013; p. 120–130.
• 148. Karduni A, Wesslen R, Santhanam S, Cho I, Volkova S, Arendt D, et al. Can You Verifi This? Studying Uncertainty and Decision-Making About Misinformation Using Visual Analytics. In Proc of the 12th International AAAI Conference on Web and Social Media (ICWSM). 2018;12(1).
• 149. Basol M, Roozenbeek J, van der Linden S. Good news about bad news: gamified inoculation boosts confidence and cognitive immunity against fake news. Journal of Cognition . 2020;3(1).
• 151. Tambuscio M, Ruffo G, Flammini A, Menczer F. Fact-checking effect on viral hoaxes: A model of misinformation spread in social networks. In Proc of the 24th International Conference on World Wide Web (WWW). 2015; p. 977–982.
• 152. Friggeri A, Adamic L, Eckles D, Cheng J. Rumor cascades. In Proc of the 8th International AAAI Conference on Weblogs and Social Media (ICWSM) . 2014;8.
• 153. Lerman K, Ghosh R. Information contagion: An empirical study of the spread of news on digg and twitter social networks. arXiv . 2010; p. arXiv–1003.
• 155. Cantril H. The invasion from Mars: A study in the psychology of panic. Transaction Publishers; 1952.
• 158. Bailey NT, et al. The mathematical theory of infectious diseases and its applications. Charles Griffin & Company Ltd; 1975.
• 159. on Religion PF, Life P. Growing Number of Americans Say Obama Is a Muslim; 2010.
• 160. Craik KJW. The nature of explanation. Cambridge University Press; 1943.
• 161. Johnson-Laird PN. Mental models: Towards a cognitive science of language, inference, and consciousness. 6. Harvard University Press; 1983.
• 162. Johnson-Laird PN, Girotto V, Legrenzi P. Mental models: a gentle guide for outsiders. Sistemi Intelligenti . 1998;9(68).
• 164. Rouse WB, Morris NM. On looking into the black box: Prospects and limits in the search for mental models. Psychological Bulletin . 1986;100(3).
• 166. Wash R, Rader E. Influencing mental models of security: a research agenda. In Proc of the 2011 New Security Paradigms Workshop (NSPW). 2011; p. 57–66.
• 167. Tversky B. Cognitive maps, cognitive collages, and spatial mental models. In Proc of European conference on spatial information theory (COSIT). 1993; p. 14–24.
• 169. Mayer RE, Mathias A, Wetzell K. Fostering understanding of multimedia messages through pre-training: Evidence for a two-stage theory of mental model construction. Journal of Experimental Psychology: Applied . 2002;8(3).
• 172. Morgan MG, Fischhoff B, Bostrom A, Atman CJ, et al. Risk communication: A mental models approach. Cambridge University Press; 2002.
• 174. Kang R, Dabbish L, Fruchter N, Kiesler S. “My Data Just Goes Everywhere:” User mental models of the internet and implications for privacy and security. In Proc of 11th Symposium On Usable Privacy and Security . 2015; p. 39–52.
• 178. Facebook Journalism Project. Facebook’s Approach to Fact-Checking: How It Works; 2020. https://bit.ly/34QgOlj .
• 179. Sardarizadeh S. Instagram fact-check: Can a new flagging tool stop fake news?; 2019. Available from: https://bbc.in/33fg5ZR .
• 180. Greenfield S. Mind change: How digital technologies are leaving their mark on our brains. Random House Incorporated ; 2015.
• 181. European Commission. European Media Literacy Week; 2020. https://bit.ly/36H9MR3 .
• 182. Media Literacy Now. U.S. media literacy policy report 2020; 2020. https://bit.ly/33LkLqQ .

An official website of the United States government

Official websites use .gov A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

• Publications
• Account settings
• Advanced Search
• Journal List

The impact of fake news on social media and its influence on health during the COVID-19 pandemic: a systematic review

Yasmim mendes rocha, gabriel acácio de moura, gabriel alves desidério, carlos henrique de oliveira, francisco dantas lourenço, larissa deadame de figueiredo nicolete.

• Author information
• Article notes
• Copyright and License information

Corresponding author.

Received 2021 Jun 15; Accepted 2021 Sep 20.

This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

As the new coronavirus disease propagated around the world, the rapid spread of news caused uncertainty in the population. False news has taken over social media, becoming part of life for many people. Thus, this study aimed to evaluate, through a systematic review, the impact of social media on the dissemination of infodemic knowing and its impacts on health.

A systematic search was performed in the MedLine, Virtual Health Library (VHL), and Scielo databases from January 1, 2020, to May 11, 2021. Studies that addressed the impact of fake news on patients and healthcare professionals around the world were included. It was possible to methodologically assess the quality of the selected studies using the Loney and Newcastle–Ottawa Scales.

Fourteen studies were eligible for inclusion, consisting of six cross-sectional and eight descriptive observational studies. Through questionnaires, five studies included measures of anxiety or psychological distress caused by misinformation; another seven assessed feeling fear, uncertainty, and panic, in addition to attacks on health professionals and people of Asian origin.

By analyzing the phenomenon of fake news in health, it was possible to observe that infodemic knowledge can cause psychological disorders and panic, fear, depression, and fatigue.

Keywords: Covid-19, Fake news, Health, Infodemic knowing

Introduction

Coronavirus 2019 disease (COVID-19), caused by the SARS-CoV-2 virus, led to the emergence of a pandemic, with a shift in economics, disruption in education, and various rules on home confinement (Munster et al. 2020 ). In this context of uncertainty, there was a need for new information about the virus, clinical manifestations, transmission, and prevention of the disease (Eysenbach 2020 ).

The rapid implementation of these measures, together with the number of significant deaths caused by the virus, ended up causing uncertainty in the population (Tangcharoensathien et al. 2020 ). In association with the generalized panic and the constant concern that COVID-19 caused, this culminated in the appearance of physical and psychological disorders, in addition to reduced immunity in the general population (Lima et al. 2020 ).

Previous studies indicate that the emergence of the pandemic and measures of social confinement caused the number of patients and health professionals with anxiety, sleep disorders and depression to increase; in addition, suicide rates were also considered high (Choi et al. 2020 ; Okechukwu et al. 2020 ). However, the use of social media and search queries to obtain information about the course of the disease is constantly expanding, and includes Twitter, Facebook and Instagram, Google Trends, Bing, Yahoo, and other more popular sources such as blogs, forums, or Wikipedia (Depoux et al. 2020 ).

Thus, information overload accompanied by fabricated and fraudulent news, also called fake news (FN), has emerged in the twentieth century to designate the fake news produced and published by mass communication vehicles such as social media, dominating traditional and social platforms, becoming increasingly part of many people’s daily lives. FNs multiply rapidly and act as narratives that omit or add information to facts (Naeem et al. 2020 ).

The potential effect of FN stems from conspiracy theories, such as a biological weapon produced in China, water with lemon or coconut oil that could kill the virus, or drugs, which even if approved for other indications, could have potential effectiveness in prevention or treatment of COVID-19. Therefore, the impact of this massive dissemination of disease-related information is known as “infodemic knowledge” (Hua and Shaw 2020 ). Other worrisome examples of infodemic knowledge include cases of hydroxychloroquine overdose in Nigeria, drug shortages, changing treatment of patients with rheumatic and autoimmune diseases, and panic over supplies and fuel (CNN 2020 ; Tentolouris et al. 2021 ).

The World Health Organization (WHO 2020 ) has worked closely to track and respond to the most prevalent myths and rumors that can potentially harm public health. In this context, the objective of the study was to evaluate, through a systematic review, the impact of the media and the media during the pandemic caused by the new coronavirus, and to determine how the spread of infodemic impacts people’s health.

This is a systematic literature review that aimed to use explicit and systematic methods to avoid the chance of risk of bias (Donato and Donato 2019 ). Therefore, the study followed a design according to the guidelines of Preferred Report items for Systematic Reviews (PROSPERO) and PRISMA Meta-analyses (PRISMA 2021 ) and the search procedures were filed in the database and registered in PROSPERO: CRD42021256508 (PROSPERO 2021 ).

Searching strategy

Search strategies were developed from the identification of relevant articles using the Medical Subjects Headings (MeSH) in a combination of Boolean AND. The search by string and keyword was calculated as follows: “Covid-19” OR “SARS-CoV-2” AND “fake news” AND “health” OR “Covid-19” AND “fake news” OR “misinformation” AND “health”. The strategy was performed using MedLine, Virtual Health Library (VHL), and Scielo databases. Search results were revised to prevent duplicate studies. The articles obtained were analyzed for relevance and step-by-step, as illustrated in Fig.  1 . The report items for systematic review illustrate the PRISMA (PRISMA 2021 ) process used to report the results.

Search strategy flowchart

Inclusion and exclusion criteria

The search terms were oriented according to the Population, Intervention, Comparison, Results and Study Design (PICOS) approach, methodology used to select the studies included in the systematic search (Methley et al. 2014 ), as shown in Table 1 . Cross-sectional studies, of cohorts or clinicians that addressed the impact of fake news on patients and health professionals around the world, were used. On the other hand, studies that did not refer to the proposed theme, review articles, or were letters and opinions were excluded. In addition, only full articles written in English, Portuguese (Brazil), and Spanish, published between January 1, 2020, and May 11, 2021, were reviewed.

Approach to study selection (PICO) following systematic search

Database searched in May 2021

Assessment of risk of bias in included studies

Internal quality was performed based on selected study designs using two scales to independently assess the risk of bias; Newcastle–Ottawa for cohort studies and Loney scale for cross-sectional studies. In case of disagreement between two researchers, the assessment was performed by a third experienced researcher (Santos et al. 2019 ). The assessment of the risk of bias between studies was assessed as shown in Tables  2 and 3 .

Methodological quality of cross-sectional studies (Loney Scale)

Questions in header relate to different criteria of quality as measured by the Loney Scale:

1 – Is the study design and sampling appropriate to answer the research question? 2 – Is the sample base adequate? 3 – Is the sample size adequate? 4 – Are adequate and standardized objective criteria used to measure motor development? 5 – Was EDM applied in an unbiased way? 6 – Is the response rate adequate? 7 – Were the EDM results presented in a detailed way? 8 – Are participants and context described in detail and can they be generalized to other situations?

Numbers alongside each reference relate to quality of response questions above: 1 = adequate, 2 = inadequate

Methodological quality on the Newcastle–Ottawa Scale (NOS)

Questions in header relate to different criteria of quality as measured by the NOS:

Selection 1: representativeness of the exposed cohort; Selection 2: selection of the unexposed cohort; Selection 3: exposure determination; Selection 4: demonstration that the result of interest was not present at baseline; Comparability 1a and 1b: comparability of cohorts based on design or analysis; Results 1: result evaluation; Results 2: follow-up of cohorts; Results 3: adequacy of cohort follow-up

Data extraction

After collecting data from the articles, they were extracted and tabulated according to the information cited later:

Type of study.

Source of FNs.

Impact of FNs on health.

Age of participants.

Country of origin.

Number of patients.

Study selection

The search strategy identified 1644 publications through the MedLine database, the Virtual Health Library (VHL), and Scielo databases. Of these studies found, 24 were removed for being duplicative and 1606 for being within the exclusion criteria. Based on this, 14 studies met the inclusion criteria and were suitable to be considered in the present review, as shown in Fig. 1 .

Study characteristics

Of all the studies included, six were cross-sectional (Ruiz-Frutos et al. 2020 ; Islam et al. 2020 ; Talwar et al. 2020 ; Sallam et al. 2020 ; Duplaga 2020 ; Secosan et al. 2020 ) and eight were descriptive observational studies (Radwan et al. 2020 ; Sun et al. 2020 ; Ahmad and Murad 2020 ; Almomani and Al-Qur’an 2020 ; Roozenbeek et al. 2020 ; Montesi 2020 ; Schmidt et al. 2020 ; Fernández-Torres et al. 2021 ). The sample size of the fourteen selected articles was a total of 571,729 participants, 1467 false new items, and 2508 reports. Most participants were over 18 years of age. The studies were conducted in 14 different countries, including Palestine ( n  = 1), Spain ( n  = 4), India ( n  = 1), Bangladesh ( n  = 1), Iraq ( n  = 1), Mexico ( n  = 1), United States of America ( n  = 1), United Kingdom ( n  = 1), Ireland ( n = 1), Jordan ( n  = 2), China n  = 1), South Africa ( n  = 1), Poland ( n  = 1) and Romania ( n  = 1), each study being able to evaluate more than one country. Other characteristics of the study and the results of the primary study are summarized in Table 4 .

Characteristics of study samples and risk factors associated with fake news

*Possible significant effect of the relationship between fake news and people older than 76 years because they are more likely to be influenced by fake news and to spread such information

The potential risks of misinformation

The results included varied in our review. It was possible to identify that misinformation could trigger varied disturbances to an individual’s perception of FNs. In five papers, the population was observed to be more prone to fearful situations (Talwar et al. 2020 ; Ahmad and Murad 2020 ; Almomani and Al-Qur’an 2020 ; Schmidt et al. 2020 ; Fernández-Torres et al. 2021 ). Consequently, two studies found that a proportion of these patients who reported being afraid because of the influence of FNs reported being confused as to the veracity of this transmitted information (Schmidt et al. 2020 ; Fernández-Torres et al. 2021 ). Our review also found that this situation of fear and confusion can lead to the onset of panic (Talwar et al. 2020 ; Radwan et al. 2020 ; Duplaga 2020 ; Ahmad and Murad 2020 ; Almomani and Al-Qur’an 2020 ; Schmidt et al. 2020 ). In which, the set between the perceptual aspects to these FN can lead to milder symptoms such as fatigue (Islam et al. 2020 ), stress (Secosan et al. 2020 ; Radwan et al. 2020 ), insomnia (Secosan et al. 2020 ), and anger (Radwan et al. 2020 ). The literature also informs us that in addition to milder symptoms inherent to a state of confusion with regard to perceived misinformation conveyed, there is a likelihood of more complex symptomatologies as was reported in five studies with an increase in the number of patients with anxiety (Ruiz-Frutos et al. 2020 ; Sallam et al. 2020 ; Secosan et al. 2020 ; Radwan et al. 2020 ; Sun et al. 2020 ). Patients have also reported being affected by depression processes inherent to these FNs (Secosan et al. 2020 ; Radwan et al. 2020 ).

Susceptibility to spreading fake news according to education and age of the population

To understand the behavior of rumor spreading among the population, our findings reveal that the age of the patients who participated in the study varied mainly between 18 and 60 years, which could infer that a good portion of individuals in different age groups could be susceptible to FN spread through social media. However, in a single study, it was found that people over the age of 76 were more susceptible to being influenced by fake news as well as spreading this information (Sun et al. 2020 ). Another important finding in the literature indicates that susceptibility to interacting with FN is independent of the individual educational level of each study subject, where in four studies it was observed that the patients involved were in secondary school (Duplaga 2020 ; Radwan et al. 2020 ; Sun et al. 2020 ), five studies addressed the susceptibility of undergraduate patients to FN (Sallam et al. 2020 ; Duplaga 2020 ; Secosan et al. 2020 ; Sun et al. 2020 ; Fernández-Torres et al. 2021 ), and in two studies graduate patients were observed (Sun et al. 2020 ; Fernández-Torres et al. 2021 ).

Content and propagation of fake news circulating on social networking platforms

It was possible to verify in the selected articles that the social network Facebook had the greatest participation in the selected studies (Islam et al. 2020 ; Sallam et al. 2020 ; Fernández-Torres et al. 2021 ), followed by Youtube in three studies (Islam et al. 2020 ; Sallam et al. 2020 ; Fernández-Torres et al. 2021 ) and WhatsApp in three more studies (Sallam et al. 2020 ; Radwan et al. 2020 ; Fernández-Torres et al. 2021 ); Twitter appeared in only one study (Sallam et al. 2020 ). Among the main FNs, we had the disclosure that the consumption of food, vitamins, and beverages improved the clinical condition of the affected patient, in addition to reducing the contamination rate (Islam et al. 2020 ; Secosan et al. 2020 ). In other studies, the infection improved with the use of mouthwashes and cutaneous substances (Sun et al. 2020 ; Almomani and Al-Qur’an 2020 ). News related to viral spread, such as the creation of the virus in the laboratory and the spread of the virus by vectors such as mosquitoes, were also addressed (Ahmad and Murad 2020 ; Roozenbeek et al. 2020 ; Montesi 2020 ). Vaccines have also become targets of fake news in studies (Montesi 2020 ).

In the context of the pandemic, the media emerged to seek information about the disease. However, many occurrences were false news masquerading as reliable disease prevention and control strategies, which created an overload of misinformation. In this process, there was interference in the behavior and health of people, generating social unrest associated with violence, distrust, social disturbances, and attacks on health professionals (Moscadelli et al. 2020 ; Apuke and Omar 2021 ).

Overall, our review suggests that people of different nationalities were affected by sharing unverified information. In all the studies included, totaling 1467 news and 2508 reports, the results show that people trust the information they find on social networks, and through these accounts ended up believing and being affected by this information. Only one author pointed out that the news did not represent a danger to people’s health and safety, being considered harmless. This fact was explained by Aleinikov et al. ( 2020 ) pointing out that in this delicate process, the important thing is to relate the perception of risk found in social media and trust in the information provided by institutions (Aleinikov et al. 2020 ).

These tools, while becoming increasingly popular, are also increasingly exposed to unreliable information. As a result, people feel anxious, depressed, or emotionally exhausted, and these expressive health effects are directly associated with the spread of this information (Lin et al. 2020 ). So much so, that when analyzing our data, it was realized that this interaction can come with both mild effects and more serious psychological problems. This is also consistent with the literature, according to Jiang ( 2021 ), who evaluated the possible psychological impact of social media on students during the pandemic and found an increase in the anxiety levels of these students, as well as a worsening in academic performance and physical exhaustion (Jiang 2021 ).

The proliferation of false news has consequences for public health because it fuels panic among people and discredits the scientific community in the eyes of public opinion. For example, a popular myth that consumption of pure alcohol — methanol — could eliminate the virus in the contaminated body killed approximately 800 people in Iran, while another 5876 people were hospitalized for methanol poisoning (Hassanian-Moghaddam et al. 2020 ). As demonstrated in our evaluation, Almomani and Al-Qur’an 2020 and Secosan et al. 2020 , in their reports also claim that the participants, in fact, believed that alcohol consumption cured COVID-19 (Secosan et al. 2020 ; Almomani and Al-Qur’an 2020 ).

Based on the literature, even social media that play a significant role in disseminating true news about COVID-19 have also been linked to illness, because as platforms that help to spread public health messages to people, they also promote opinionated reporting. and concerns about the disease (Galea et al. 2020 ). In fact, the results pointed out in this review reveal that 36% of the authors showed that exposure to infodemic knowledge generated fear, panic, depression, stress, and anxiety in people interviewed through an online questionnaire. This is corroborated by a cross-sectional study carried out by Olagoke et al. ( 2020 ), that when evaluating 501 participants, the anxiety and depression score was related to news exposure in the traditional media, showing a prevalence of depressive symptoms and a greater perceived vulnerability, causing great psychological impact.

Our results indicate that different age groups have susceptibility to interact with the FN propagated by social media, especially in the elderly population. These results were also verified in a previous study by Guimarães et al. 2021 , who aimed to assess the population’s knowledge about COVID-19 and misinformation from an anonymous online survey and, with this, some parameters such as gender, education, and age were shown to be directly associated with a better perception of health issues in the context of the pandemic (Guimarães et al. 2021 ). The same was also seen by Hayat et al. 2020 , who explored the public’s understanding of the current situation of the COVID-19 from online forms and concluded that participants with ages ranging from 16 to 29 years obtained better scores than older participants (Hayat et al. 2020 ). Such a fact is associated with the digital media literacy of individuals primarily over the age of 60 who end up not reliably determining the trustworthiness of online news, thus needing to develop literacy competencies that encompass the types of skills needed to identify questionable content (Guess et al. 2019 ).

To understand the behavior of spreading rumors among the elderly population, our results show that most respondents (74.82%) negatively evaluated the dissemination of fake news, while 2.52% did not care anyway. Among them, the correlation between the spread of rumors and anxiety was negatively associated, as they influence the behavior and perception of the elderly to understand what a fact is and what is fake news. Research shows that individuals over 65 years share up to seven times more unverified information when compared to other age groups, often in order to feel useful, active, and connected (Guess et al. 2019 ). Certainly, psychological interventions are mainly recommended to vulnerable populations and health professionals (Van Der Linden et al. 2020 ).

Our results also showed that 36% of the authors reported that, regardless of age, it was possible for participants to experience fatigue, anguish, and psychological distress, in addition to having a higher probability of developing anxiety-related symptoms. This is contradicted in two previous studies by Huang and Zhao ( 2020 ) and Wang et al. ( 2020 ); when evaluating the psychological impact of the uncontrolled spread of COVID-19, they realized that the manifestations of anxiety and psychological outbreaks were more common especially in the younger population who used social networks for a longer time (Huang and Zhao 2020 ; Wang et al. 2020 ). On the other hand, pandemic uncertainty and confinement created considerable levels of stress in young people, especially women, in Switzerland (Mohler-Kuo et al. 2021 ). It was further shown that misinformation fueled by rumors and conspiracy theories led to physical harassment and violent attacks against healthcare professionals and people of Asian origin in 28% of the results shown in this review. This finding is in line with a study that shows that conspiracy theories are not a new phenomenon, but they increase in times of crisis. Thus, people who believe in this “conspiracy world” are less likely to comply with social norms (Imhoff and Lamberty 2020 ).

The impact of denial and its association with fake news presents itself as a social phenomenon through the production of controversial theses to the scientific consensus (Duarte and César 2020 ). Good examples of denial content can be the emergence of the earthmoving movement, the global warming farce, and anti-vaccination discourses (Vasconcelos-Silva and Castiel 2020 ). With regard to the COVID-19 pandemic, denialism takes on an expression never seen before, in which the number of people who spread this news grows more and more, and therefore results in an increase in the number of deaths of the most vulnerable patients (Morel 2021 ).

Importantly, false information has been a genuine concern among social-media platforms and governments, which have implemented strategies to contain misinformation and fake news during the pandemic. Of the social-media platforms, in order to contain the advance of FNs, Facebook has implemented a new feature to inform users when they engage with unverified information (BBC 2020 ). Another way to counteract misinformation is to seek support and discuss actions that authorities or public agencies could take to mitigate the spread of conspiracy theories, and encourage users to flag inappropriate content to social-media companies (González-Padilla and Tortolero-Blanco 2020 ).

Social-media platforms have contributed to the spread of false news and conspiracy theories during the new coronavirus pandemic. When analyzing the phenomenon of fake news in health, it is possible to observe that infodemic knowledge is part of people’s lives around the world, causing distrust in Governments, researchers, and health professionals, which can directly impact people’s lives and health. When analyzing the potential risks of misinformation, panic, depression, fear, fatigue, and the risk of infection influence psychological distress and emotional overload. In the COVID-19 pandemic, the disposition to spread incorrect information or rumors is directly related to the development of anxiety in populations of different ages.

Acknowledgments

The authors would like to thank the CAPES and FUNCAP for the fellowships of Yasmim M Rocha and Gabriel A de Moura.

Author contributions

Yasmim Mendes Rocha: bibliographic research, concepts, methodology, writing, and data analysis. Gabriel Acácio de Moura: bibliographic research, methodology, revision, editing, and data analysis. Gabriel Alves Desidério: reading of included articles and review. Carlos Henrique de Oliveira: translation into English, reading of articles, and writing. Francisco Dantas Lourenço: article reading and review. Larissa Deadame de Figueiredo Nicolete: article idea, supervision, methodology, research, formal analysis, and editing.

This study was supported by the Coordination for the Improvement of Higher Education Personnel (CAPES) and the Cearense Foundation for Scientific and Technological Development Support (FUNCAP).

Declarations

Conflict of interest.

The authors declare no conflict of interest.

This is a review article that has not been published before and is not being considered for publication anywhere. Authors confirm that the manuscript has been read and approved by all named authors and that no other person has met the authorship criteria, but it is not listed. We further confirm that none of us have any conflict of interest to declare. We would like to thank you for your attention and the opportunity given to submit our study, and agree, if the manuscript is accepted for publication, to the transfer of all copyright to the Journal of Public Health .

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

• Ahmad AR, Murad HR. The impact of social media on panic during the COVID-19 pandemic in Iraqi Kurdistan: online questionnaire study. J Medical Internet Res. 2020;22:e19556. doi: 10.2196/19556. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Aleinikov AV, Maltseva DA, Sunami AN. Information management of the risks and threats of the Covid-19 pandemic. Sci Tech Inf Process. 2020;47:200–206. doi: 10.3103/S0147688220030090. [ DOI ] [ Google Scholar ]
• Almomani H, Al-Qur’an W (2020) The extent of people's response to rumors and false news in light of the crisis of the Corona virus. Ann Med Psychol 178(7):684–689. 10.1016/j.amp.2020.06.011 [ DOI ] [ PMC free article ] [ PubMed ]
• Apuke OD, Omar B. Fake news and COVID-19: modelling the predictors of fake news sharing among social media users. Telemat Inform. 2021;56:101475. doi: 10.1016/j.tele.2020.101475. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• BBC News (2020) Coronavirus: Facebook alters virus action after damning misinformation report (Joe Tidy, cyber-security reporter). https://www.bbc.com/news/technology-52309094
• Choi EPH, Hui BPH, Wan EYF. Depression and anxiety in Hong Kong during COVID-19. Int J Environ Res Public Health. 2020;17:3740–3750. doi: 10.3390/ijerph17103740. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• CNN (2020) Nigeria records chloroquine poisoning after Trump endorses it for coronavirus treatment. Busari S, Adebayo B. CNN International, 23 March 2020. Available in: https://edition.cnn.com/2020/03/23/africa/chloroquine-trump-nigeria-intl/index.htm . February 21, 2021, date of last access
• Depoux A, Martin S, Karafillakis E, Preet R, Wilder-Smith A, Larson H (2020) The pandemic of social media panic travels faster than the COVID-19 outbreak. J Travel Med 27(3):taaa031. 10.1093/jtm/taaa031 [ DOI ] [ PMC free article ] [ PubMed ]
• Donato H, Donato M (2019) Stages for undertaking a systematic review. Acta Med Port 32: 227–235. 10.20344/amp.11923 [ DOI ] [ PubMed ]
• Duarte AM, César MRA. Negação da política do negacionismo como política: Pandemia e democracia. Educ Realid. 2020;45:e109146. doi: 10.1590/2175-6236109146. [ DOI ] [ Google Scholar ]
• Duplaga M. The determinants of conspiracy beliefs related to the COVID-19 pandemic in a nationally representative sample of internet users. Int J Environ Res Public Health. 2020;17:7818. doi: 10.3390/ijerph17217818. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Eysenbach G. How to fight an infodemic: the four pillars of infodemic management. J Med Internet Res. 2020;22:e21820. doi: 10.2196/21820. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Fernández-Torres MJ, Almansa-Martínez A, Chamizo-Sánchez R. Infodemic and fake news in Spain during the COVID-19 pandemic. Int J Env Res Public Health. 2021;18:1781. doi: 10.3390/ijerph18041781. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Galea S, Merchant RM, Lurie N. The mental health consequences of COVID-19 and physical distancing: the need for prevention and early intervention. JAMA Intern Med. 2020;180:817–818. doi: 10.1001/jamainternmed.2020.1562. [ DOI ] [ PubMed ] [ Google Scholar ]
• González-Padilla DA, Tortolero-Blanco L. Social media influence in the COVID-19 pandemic. Int Braz J Urol. 2020;46:120–124. doi: 10.1590/S1677-5538.IBJU.2020.S121. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Guess A, Nagler J, Tucker J. Less than you think: prevalence and predictors of fake news dissemination on Facebook. Sci Adv. 2019;5:eaau4586. doi: 10.1126/sciadv.aau4586. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Guimarães V, Leandro M, Cassiano C, Marques A, Motta C, Silva A, Sousa M, Silveira L, Pardi T, Gazotto F, Silva M, Rodrigue V, Jr, Rodrigues W, Oliveira C. Knowledge about COVID-19 in Brazil: cross-sectional web-based study. JMIR Public Health Surveill. 2021;7:e24756. doi: 10.2196/24756. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Hassanian-Moghaddam H, Zamani N, Kolahi A-A, McDonald R, Hovda KE. Double trouble: methanol outbreak in the wake of the COVID-19 pandemic in Iran—a cross-sectional assessment. Crit Care. 2020;24:1–3. doi: 10.1186/s13054-020-03140-w. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Hayat K, Rosenthal M, Xu S, Arshad M, Li P, Zhai P, Desalegn G, Fang Y. View of Pakistani residents toward coronavirus disease (COVID-19) during a rapid outbreak: a rapid online survey. Int J Environ Res Public Health. 2020;17:3347. doi: 10.3390/ijerph17103347. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Hua J, Shaw R. Corona virus (Covid-19)“infodemic” and emerging issues through a data lens: the case of China. Int J Env Res Public Health. 2020;17:2309. doi: 10.3390/ijerph17072309. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Huang Y, Zhao N. Generalized anxiety disorder, depressive symptoms and sleep quality during COVID-19 outbreak in China: a web-based cross-sectional survey. Psychiatry Res. 2020;288:112954. doi: 10.1016/j.psychres.2020.112954. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Imhoff R, Lamberty P. A bioweapon or a hoax? The link between distinct conspiracy beliefs about the coronavirus disease (COVID-19) outbreak and pandemic behavior. Soc Psychol Personal Sci. 2020;11:1110–1118. doi: 10.1177/1948550620934692. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Islam AKMN, Laato S, Talukder S, Sutinen E. Misinformation sharing and social media fatigue during COVID-19: an affordance and cognitive load perspective. Technol Forecast Soc Chang. 2020;159:120201. doi: 10.1016/j.techfore.2020.120201. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Jiang Y. Problematic social media usage and anxiety among university students during the COVID-19 pandemic: the mediating role of psychological capital and the moderating role of academic burnout. Front Psicol. 2021;12:76–88. doi: 10.3389/fpsyg.2021.612007. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Lima SO, Da Silva MA, Santos MLD, De Jesus CVF. Reflexão sobre o estado físico e mental dos profissionais de saúde em época de Covid-19. Interfaces Científicas-Saúde e Ambiente. 2020;8:142–151. doi: 10.17564/2316-3798.2020v8n2p142-151. [ DOI ] [ Google Scholar ]
• Lin Y, Hu Z, Alias H, Wong LP. Influence of mass and social media on psychobehavioral responses among medical students during the downward trend of COVID-19 in Fujian, China: cross-sectional study. J Med Internet Res. 2020;22(7):e19982. doi: 10.2196/19982. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Methley AM, Campbell S, Chew-Graham C, McNally R, Cheraghi-Sohi S (2014) PICO, PICOS and SPIDER: a comparison study of specificity and sensitivity in three search tools for qualitative. BMC Health Serv Res 14:579. 10.1186/s12913-014-0579-0 [ DOI ] [ PMC free article ] [ PubMed ]
• Mohler-Kuo M, Dzemaili S, Foster S, Werlen L, Walitza S. Stress and mental health among children/adolescents, their parents, and young adults during the first COVID-19 lockdown in Switzerland. Int J Env Res Public Health. 2021;18:4668. doi: 10.3390/ijerph18094668. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Montesi M (2020) Understanding fake news during the Covid-19 health crisis from the perspective of information behaviour: the case of Spain. J Librariansh Inform Science 2020:0961000620949653. 10.1177/0961000620949653
• Morel A (2021) Negacionismo da Covid-19 e educação popular em saúde: para além da necropolítica. Trab Educ Saúde 19. 10.1590/1981-7746-sol00315
• Moscadelli A, Albora G, Biamonte MA, et al. Fake news and Covid-19 in Italy: results of a quantitative observational study. Int J Env Res Public Health. 2020;17:5850. doi: 10.3390/ijerph17165850. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Munster VJ, Koopmans M, Doremalen NV, Riel DV, Wit E. A novel coronavirus emerging in China—key questions for impact assessment. N Engl J Med. 2020;382:692–694. doi: 10.1056/NEJMp2000929. [ DOI ] [ PubMed ] [ Google Scholar ]
• Naeem SB, Bhatti R, Khan A (2020) An exploration of how fake news is taking over social media and putting public health at risk. Health Inf Libr J 38(2):143–149. 10.1111/hir.12320 [ DOI ] [ PMC free article ] [ PubMed ]
• Okechukwu EC, Tibaldi L, Torre GL (2020) The impact of COVID-19 pandemic on mental health of nurses. Clin Ter 171 (5):e399–e400. 10.7417/CT.2020.2247 [ DOI ] [ PubMed ]
• Olagoke AA, Olagoke OO, Hughes AM. Exposure to coronavirus news on mainstream media: the role of risk perceptions and depression. Br J Health Psychol. 2020;25:865–874. doi: 10.1111/bjhp.12427. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• PRISMA (2021) Prisma Statement. http://www.prisma-statement.org/PRISMAStatement. Access in: May 11,2021. systematic reviews. BMC Health Serv Res 14(1):1–10
• PROSPERO (2021) International prospective register of systematic reviews. University of York, York, UK. https://www.crd.york.ac.uk/prospero/ . Access 11 March 202
• Radwan E, Radwan A, Radwan W. The role of social media in spreading panic among primary and secondary school students during the COVID-19 pandemic: an online questionnaire study from the Gaza Strip, Palestine. Heliyon. 2020;6:e05807. doi: 10.1016/j.heliyon.2020.e05807. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Roozenbeek J, Schneider CR, Dryhurst S, et al. Susceptibility to misinformation about COVID-19 around the world. Royal Soc Open Sci. 2020;7:201199. doi: 10.1098/rsos.201199. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Ruiz-Frutos C, Ortega-Moreno M, Dias A, Bernardes JM, García-Iglesias JJ, Gómez-Salgado J. Information on COVID-19 and psychological distress in a sample of non-health workers during the pandemic period. Int J Env Res Public Health. 2020;17:6982. doi: 10.3390/ijerph17196982. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Sallam M, Dababseh D, Yaseen A, et al. COVID-19 misinformation: Mere harmless delusions or much more? A knowledge and attitude cross-sectional study among the general public residing in Jordan. PLoS One. 2020;15(12):e0243264. doi: 10.1371/journal.pone.0243264. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Santos MCS, Shimano SGN, Araújo LGO, Pereira K (2019) Application of Motor Development Scale: an integrative review. Revista CEFAC 21(4):1516–1846. 10.1590/1982-0216/20192149918
• Schmidt T, Cloete A, Davids A, Makola L, Zondi N, Jantjies M. Myths, misconceptions, othering and stigmatizing responses to Covid-19 in South Africa: a rapid qualitative assessment. PLoS One. 2020;15:e0244420. doi: 10.1371/journal.pone.0244420. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Secosan I, Virga D, Crainiceanu ZP, Bratu LM, Bratu T. Infodemia: another enemy for Romanian frontline healthcare workers to fight during the COVID-19 outbreak. Medicina. 2020;56:679. doi: 10.3390/medicina56120679. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Sun Z, Cheng X, Zhang R, Yang B. Factors influencing rumour re-spreading in a public health crisis by the middle-aged and elderly populations. Int J Env Res Public Health. 2020;17:6542. doi: 10.3390/ijerph17186542. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Talwar S, Dhir A, Singh D, Virk GS, Salo J. Sharing of fake news on social media: application of the honeycomb framework and the third-person effect hypothesis. J Retail Consum Serv. 2020;57:102197. doi: 10.1016/j.jretconser.2020.102197. [ DOI ] [ Google Scholar ]
• Tangcharoensathien V, Calleja N, Nguyen T, et al. Framework for managing the COVID-19 infodemic: methods and results of an online, crowdsourced WHO technical consultation. J Med Inter Res. 2020;22:e19659. doi: 10.2196/19659. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Tentolouris A, Ntanasis-Stathopoulos I, Vlachakis PK, Tsilimigras DI, Gavriatopoulou M, Dimopoulos MA (2021) COVID-19: time to flatten the infodemic curve. Clin Exp Med:1–5. 10.1007/s10238-020-00680-x [ DOI ] [ PMC free article ] [ PubMed ]
• Van Der Linden S, Roozenbeek J, Compton J. Inoculating against fake news about COVID-19. Front Psychol. 2020;11:2928. doi: 10.3389/fpsyg.2020.566790. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Vasconcelos-Silva PR, Castiel LD. COVID-19, las fake news y el sueño de la razón comunicativa generando monstruos: el relato de los riesgos y los riesgos de los relatos. Cad Saúde Pública. 2020;36:e00101920. doi: 10.1590/0102-311X00101920. [ DOI ] [ PubMed ] [ Google Scholar ]
• Wang C, Pan R, Wan X, et al. Immediate psychological responses and associated factors during the initial stage of the 2019 coronavirus disease (COVID-19) epidemic among the general population in China. Int J Env Res Public Health. 2020;17:1729. doi: 10.3390/ijerph17051729. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• World Health Organization (2020) Novel Coronavirus ( 2019-nCoV): situation report, 3. 2020. World Health Organization, Geneva. https://apps.who.int/iris/handle/10665/330762 . February 20, 2021, date of last access
• View on publisher site
• PDF (413.0 KB)
• Collections

Similar articles

Cited by other articles, links to ncbi databases.

• Download .nbib .nbib
• Format: AMA APA MLA NLM

Add to Collections

• Systematic Review
• Open access
• Published: 18 November 2024

Psychological factors contributing to the creation and dissemination of fake news among social media users: a systematic review

• Shalini Munusamy 1 ,
• Kalaivanan Syasyila 2 ,
• Azahah Abu Hassan Shaari 2 ,
• Muhammad Adnan Pitchan 3 ,
• Mohammad Rahim Kamaluddin 2 &
• Ratna Jatnika 4  

BMC Psychology volume  12 , Article number:  673 ( 2024 ) Cite this article

283 Accesses

4 Altmetric

Metrics details

The proliferation of fake news on social media platforms has become a significant concern, influencing public opinion, political decisions, and societal trust. While much research has focused on the technological and algorithmic factors behind the spread of misinformation, less attention has been given to the psychological drivers that contribute to the creation and dissemination of fake news. Cognitive biases, emotional appeals, and social identity motivations are believed to play a crucial role in shaping user behaviour on social media, yet there is limited systematic understanding of how these psychological factors intersect with online information sharing. Existing studies tend to focus on individual aspects of fake news consumption, such as susceptibility to misinformation or partisan biases, leaving a gap in understanding the broader psychological mechanisms behind both the creation and dissemination of fake news. This systematic review aims to fill this gap by synthesizing current research on the psychological factors that influence social media users’ involvement in dissemination and creation of fake news. Twenty-three studies were identified from 2014 to 2024 following the PRISMA guidelines. We have identified five themes through critical review and synthesis of the literature which are personal factors, ignorance, social factors, biological process, and cognitive process. These themes help to explain the psychological factors contributing to the creation and dissemination of fake news among social media users. Based on the findings, it is evident that diverse psychological factors influence the dissemination and creation of fake news, which must be studied to design better strategies to minimize this issue.

Peer Review reports

A social phenomenon known as “fake news” shows up in the framework of social interactions. It refers to information that has been widely shared without a factual foundation, verification, or explanation, whether on purpose or accidentally [ 1 ]. This issue has raised concerns across various academic fields, including social sciences such as sociology and journalism, as well as disciplines like psychology, communication studies, political science, and information technology. Researchers in these fields are concerned about the widespread impact of fake news on public opinion, mental health, political polarization, and the integrity of information shared on digital platforms. Each discipline examines the phenomenon from different angles, such as its influence on behaviour, media credibility, societal trust, and the role of algorithms in amplifying misinformation [ 2 ]. Spread through word-of-mouth and quickly spread by the development of social media and technology, misinformation demonstrates traits including fission dissemination, high propagation speed, broad effect, and significant impact. While it is common to find fake news on illegal websites, well-known social media platforms like Facebook, WhatsApp, Telegram, Twitter, and others are important conduits for the quick dissemination of unverified material. According to Lazer et al., the spread of false information and fake news on social media platforms not only alarms the public and endangers people’s physical and mental well-being, but it also seriously disrupts the security and management of the social system [ 3 ].

Based on Fig.  1 , a Google trend analysis of the creation and dissemination of fake news for the past three years (2021–2023) shows that global fake news creation and dissemination showed a high percentage in 2021 compared to 2022 and 2023. The analysis likely involved tracking data from social media platforms, news websites, and fact-checkers, focusing on metrics such as shares, retweets, and geographic spread. Analytical tools like sentiment analysis and machine learning could have been used to detect and categorize fake news by themes such as political or health-related misinformation. The interpretation would highlight peaks in dissemination during events like elections or pandemics, regional differences in spread, and platform-specific trends, revealing how different factors and response efforts influenced the global distribution of fake news. As a result, 2021 marked a turning point for online information warfare, with fake news linked to COVID-19 being the most widespread issue globally. Upon reviewing the pertinent literature, it was discovered that almost all the research on fake news is related to detecting techniques that use machine learning algorithms to analyse social media feeds and platform features [ 4 ]. While a significant body of research has explored the spread of misinformation, much of the existing literature has focused on content analysis of social media communications, often overlooking the underlying psychological mechanisms that drive user engagement with fake news. Contrary to this narrow focus, several studies have examined audience behaviour, employing diverse methods such as surveys, experiments, and network analysis to investigate why individuals share fake news ( 5 – 6 ). Other research on the subject included sharing the history of the fake news that was identified, examining the core content of the material, and analysing information found in comments and articles [ 7 , 8 , 9 , 10 ].

Global patterns for the dissemination of fake news in 2021–2023

Other research concentrated on language traits and writing style, the sharing history of the fake news that was identified, the analysis of the content’s core ideas, and information found in articles and comments [ 11 ]. Fact-checking websites is another technical approach to detecting bogus news. To further understand the evolution pattern of the creation and dissemination of fake news in social networks, we need to understand the psychological factors that contribute to the following. Psychological elements, such as cognitive biases, emotional responses, and belief systems, fundamentally shape how individuals perceive, evaluate, and interact with information. Unlike structural or technological factors, which may facilitate the spread of fake news, psychological factors directly impact decision-making processes at the individual level. For instance, cognitive biases like confirmation bias lead people to accept and share information that aligns with their pre-existing beliefs, regardless of its accuracy [ 5 ]. Emotions such as fear, anger, or excitement can drive impulsive reactions, further amplifying the spread of misinformation [ 12 ]. Given that fake news often capitalizes on these emotional and cognitive tendencies, understanding the psychological underpinnings provides a deeper insight into why certain news is created and widely shared. Additionally, while factors like social network structure and algorithmic influence are significant, they often operate through psychological mechanisms. Therefore, a focus on psychological factors offers a more fundamental understanding of the issue, allowing for more targeted interventions to mitigate the spread of misinformation.

Psychological factors are multidimensional. They are defined as the factors such as core beliefs, emotions, anxieties, and self-perceptions that influence an individual’s behaviour and well-being. These psychological factors determine our reaction toward various situations and thus affect our behaviour [ 13 ]. Psychological factors are associated with the dissemination of fake news across social media platforms [ 14 ]. For example, emotions are one of the psychological factors that may impact how an individual reacts to bogus news. For instance, studies have shown that emotions like fear or anger significantly impact how individuals assess the credibility of information and their likelihood to share it without verification [ 12 , 15 ]. Cognitive biases, such as confirmation bias, also shape how individuals interact with fake news, as they are more likely to believe and spread information that aligns with their pre-existing beliefs [ 5 ]. By exploring each dimension in depth, researchers can better understand how these psychological factors interrelate and influence the spread of misinformation, thereby identifying where future research is needed.

According to Martel et al., the following factors influence people’s willingness to believe fake news [ 16 ]. Firstly, certain moods connected with joy or higher motivation are often related with a negative propensity to detect deceit and a tendency to believe misleading information; on the other hand, certain moods linked to melancholy or lower drive are usually linked with doubt and disbelief. Second, one is more inclined to rely upon heuristic cues to choose whether to believe the facts when one is upset than when one is angry. Third, anxiety increases one’s willingness to entertain opposing viewpoints, but anger decreases this proclivity. Besides that, altruism is also another psychological factor that can be a driving force to help others without seeking compensation for oneself. It is acknowledged as one of the key elements contributing to the dissemination of false information [ 17 , 18 ]. Altruism and anxiety are related when someone’s desire to assist others stems from concern for their welfare. Two types of research are relevant when considering altruism as a factor in a person’s vulnerability to deceit. The first category investigates the drive to gain as a factor. In addition, motivation is also another psychological factor that encompasses the fundamental reasons that individuals create and disseminate fake news [ 19 ]. The motive provided here is not only concerned with individual motivations to disseminate fake news but rather with why fake news is formed in the first place.

In terms of individual-level factors, including beliefs and other cognitive states, some empirical research indicates that recipients of false political news are unlikely to have their opinions changed because of cognitive biases that keep them from accepting stories that contradict their ideas [ 20 ]. This, however, may be unique to the extremely divisive field of politics, since studies indicate that disinformation can be corrected in less divisive fields, such as health [ 21 ]. The personal consequences of misinformation might also show themselves as people sharing these messages on social media without even assessing them. Studies have indicated that people disseminate news articles on social media platforms due to the fact that it fulfils their desire for influence and social interaction [ 22 ]. This is because some erroneous statements are sensational, and people use social media with a hedonistic perspective, which may make sharing such messages more lucrative [ 23 ].

Although there have been studies examining psychological variables that contribute to the creation and spread of fake news, no research has directly explored the specific relationship between psychological factors and the production and dissemination of fake news. Due to the enormous number of studies exploring aspects of disinformation and the creation of fake news, a review of psychological factors on fake news can help us understand the extent to which psychological factors contribute to the creation and dissemination of fake news. It will also help us to identify individuals with those factors which will assist in the reduction of creation and dissemination of fake news. Hence, this study aims to carry out an extensive review of psychological factors, which contribute to the creation and dissemination of fake news among social media users.

Methodology

This review has been registered under PROSPERO (CRD42024551727). The PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) 2020 guideline [ 24 ] was followed in conducting the systematic literature review search. This review focuses on the motivations behind the production and spread of fake news rather than how to spot it. The identification, screening, eligibility, and inclusion phases made up the four stages of this systematic review’s analysis. The Covidence tool were utilized to complete this study.

Identification of literature

During the identification stage, Web of Science, Scopus, PubMed, and PsycINFO are the four databases that were searched. These databases were chosen to ensure that a wide range of fake news dissemination and creation studies from various geographical regions were included, as these databases provide publications that fulfil some level of acceptable research criteria. Four keywords were used to create search terms: (a) psychological factors; (b) dissemination; (c) creation, and (d) fake news. To capture the vast amounts of literature in each domain, each keyword was matched with its synonyms. These terms and derivatives were then converted into search strings, as seen in Table  1 .

Eligibility and screening

Subsequently, the literature that was discovered was screened in phases. Examining the titles, abstracts, and keywords of the selected studies from the entire search string which contained all four keywords, and their synonyms was the first step. All retrieved papers were exported onto a reference manager (RIS) which was consecutively imported into Covidence, to assist with the title and abstract as well as the full-text review. We screened the titles and abstracts to shortlist relevant papers. Successively, full texts were assessed for aptness. Subsequently, the article’s entire content was carefully examined to determine whether it met the inclusion and exclusion criteria listed in Table  2 . First, only English-language articles were reviewed to streamline the review process and prevent content misunderstandings resulting from translation issues. The second is a timeline that runs from 2014 to 2024, or a decade. Furthermore, since psychological elements are the main explanatory variable in the study, only research papers that examine or address any psychological issues related to the creation and dissemination of fake news were considered.

The current review classified psychological components as elements that impact an individual’s behaviour and overall well-being, including basic beliefs, emotions, worries, and self-perceptions. These psychological elements influence our behaviour by dictating how we respond to different circumstances. Furthermore, to prevent erroneous interpretations of the findings, papers that fail to explicitly pinpoint the psychological elements connected to the production or spread of false information were disregarded. A total of 392 papers were found through the initial literature search using the literature databases, and they were filtered according to their title and abstracts. As such, 137 articles were found and chosen for additional screening after the initial screening. Following that, 33 articles were omitted because they were duplicates, together 91 articles were omitted as they didn’t address the dissemination and creation of false information. The remaining articles were examined in their entirety to evaluate if they complied with the inclusion criteria. As a result, 23 pertinent papers were chosen as the best fit to be included in this systematic review and to achieve the goal of this study, which was to determine the variables linked to unfavourable bystander behaviour. The procedure is shown in Fig.  2 PRISMA flow diagram.

PRISMA Flowchart

Quality assessment

The Covidence software was used in this study to evaluate the quality of the included research. The study design, methodological rigor, sample size, and risk of bias domains were among the evaluation criteria that were predetermined in accordance with the research question. The evaluation of studies was based on how well they adhered to established quality standards in their respective domains. For documenting assessments on different bias domains, such as reporting bias, attrition bias, detection bias, performance bias, and selection bias, Covidence offered an organised framework. Before the formal assessment, a calibration exercise was carried out to guarantee consistency and dependability in the quality assessment procedure. Through consensus sessions, reviewers addressed differences and improved their comprehension of the assessment criteria. Using Cohen’s kappa coefficient to measure inter-rater reliability, significant agreement was found (κ = 0.80). Every included study was assessed in relation to the predetermined standards, and assessments of the potential for bias were noted. With few cases of notable bias, most studies showed moderate to high methodological quality overall.

The data was analysed and interpreted using 23 peer-reviewed publications. The primary findings of all the chosen research focused on the psychological elements that influence the production or spread of false information. Table  3 provides an overview of the general features of the included studies as well as their research findings. A study used the longitudinal design [ 25 ], while the rest of the study assessed were cross-sectional studies. A large sample size was employed in twenty-two studies using a quantitative technique while only small sample size was included in two studies using a qualitative approach [ 26 , 27 ]. The studies were from Malaysia [ 17 , 19 ], Nigeria ( 18 , 28 – 29 ), United Kingdom [ 25 , 26 ], United States of America [ 30 , 31 , 32 , 33 , 34 , 35 ], India ( 27 , 36 – 37 ), France [ 38 ], Vietnam [ 39 ], China [ 40 , 41 ], Israel [ 42 ], Korea [ 43 ], and Singapore [ 44 ]. In this review, we had found different types of psychological factors, which can contribute to creation and sharing fake news among social media users. However, out of the 23 articles included in this study, only one article [ 32 ] explored on the creation of fake news.

Additionally, we looked at psychological aspects such as biological processes (e.g., eat, pain, love, emotion), social processes (e.g., family and friends), cognitive processes (e.g., think, cause, thoughts, attitudes, belief), and personal concerns (e.g., work, leisure, achieve, home, money, religion, death, motivations, intention). The study of the data showed that the variables most frequently researched were those that the current review classified as “personal factors” in relation to the elements linked to creation and distributing fake news, while social effects were the variables that were studied the least. Emotional aspects have also been covered in numerous studies. Subsequently, the evaluation process yielded factors linked to the creation and dissemination of fake news, which were then classified into four groups: (a) individual characteristics; (b) cognition; (c) affection; and (d) societal processes. The psychological elements are categorized based on their domain in Table  4 .

Findings and discussion

Individual characteristics.

Few studies show evidence that altruism contributes to sharing fake news among social media users [ 17 , 18 , 19 , 39 ]. The driving force behind altruism is the desire to better the welfare of another person to better one’s own well-being. Altruism and associated concepts such as collaboration and reciprocity are commonly regarded as uniquely human characteristics [ 45 ]. Besides that, altruism influences the decision to disseminate fake news on social media platforms [ 46 ]. Previous studies have also discovered that social media information dissemination is influenced by benevolence [ 47 ]. This indicates that a selfless person finds satisfaction in helping others. However, we argue that people may contribute to the propagation of false information if they do not pay closer attention to what is being shared. In a study done by Apuke and Omar [ 18 ], it has been shown that altruism is a distinct trait of the average Nigerian, and it is more of a cultural characteristic [ 18 ]. Motivation is another psychological factor, which helps in the dissemination of fake news ( 17 – 18 , 26 , 32 , 36 – 37 ). Motivation is defined as the energy that drives someone to complete a goal. Motivation is frequently motivated by some rationale where it makes sense in the context of the individual [ 48 ].

The motive behind disseminating fake news is also linked to elements including social media weariness, self-promotion, internet confidence, self-reporting, and fear of missing out [ 17 , 36 ]. Self-promotion drive is a type of motivation that appears when users want to show other users that they are highly competent or that they are intelligent or skilled [ 18 , 49 ]. While promoting oneself is linked to projecting a positive image to others, this could discourage people from spreading false information [ 36 ]. Furthermore, studies have shown that those who spread false information out of a fear of missing out are more inclined to do so [ 36 ]. These findings go counter to the theory that exclusion anxiety, one of the factors influencing fear of missing out, causes a decline in self-regulation and an increase in undesirable behaviour [ 50 ]. This implies that people may share fake news online because of a need to use social media in spite of fatigue, as it can be an easy way to keep engaged without putting in a lot of work. Moreover, they might unknowingly disseminate false information. This result is in line with the findings of Logan et al., who contended that users experiencing social media fatigue make more mistakes and get confused [ 51 ]. Additionally, the sociotechnical model of media effects predicts that tired users will disseminate false information.

In terms of creation fake news, Shehata and Eldakar [ 32 ] had discovered that motivation demonstrated that individuals create false material on social media for a variety of reasons, while obtaining pertinent information and lowering anxiety were the primary drivers. The study’s findings suggested that although some social media users may create false information for other purposes, such as amusement, forming and strengthening friendships, or engaging with friends, the majority of users did so to obtain pertinent updates and feel less anxious [ 32 ]. Recent scholarly research has emphasised the various modern aspects that impact the production of fake news. The influence of social media platforms is one important component. Research has demonstrated that these platforms’ algorithm-driven content promotion helps spread disinformation quickly [ 52 ]. Furthermore, “echo chambers” on social media are a phenomenon that aids in the dissemination of fake news since people are more likely to come across and accept content that confirms their preconceived notions [ 53 ]. Fake news is also created and for the need for social approval and the attraction of sensational information [ 54 ]. These results highlight the intricate interactions of social, technological, and psychological elements in the creation of fake news.

Personality [ 25 , 34 , 38 , 41 ]. Psychology uses personality to categorize several individual characteristics that affect behaviour [ 55 ]. All of them used the Big Five personality model to explain the personality characteristics of sharing fake news [ 21 , 30 , 34 , 37 ] exhibiting traits of big five personality which are Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. The qualities of this five trait or dimension are derived from research into how people identify themselves and one another in normal or everyday language [ 55 ]. From these findings, we found that the individuals who disseminate fake news have high levels of Agreeableness, Extraversion, and Neuroticism. While extraversion and agreeableness traits are consistent with previous research on the relationship between social media engagement and personality [ 56 , 57 ]. The background of the COVID-19 epidemic, which sends messages of dread, loneliness, melancholy, anger, uncertainty, and grief, among other things, may help to explain why neuroticism initially seems odder. The information source and agreeableness attribute may interact, with more pleasant people being more driven to please those who are important to them. The stories might be important, and misinformation products are usually adversarial or critical in character. This may suggest that they are more likely to be shared by irascible individuals who don’t care to apologize to others or take offence at them. Moreover, agreeableness and general trusting behaviour are related. It may be that individuals who are unpleasant are more likely to read conspiratorial literature or other materials that are consistent with a lack of faith in public figures such as politicians.

However, a study done by Lawson and Kakkar [ 34 ], only explored the role of conscientiousness against fake news sharing behaviour. Besides that, individuals with lower conscientiousness are more inclined to share fake news, which is not surprising as they are less likely to verify the authenticity of a story before sharing it. This viewpoint is reinforced by the absence of a link with deliberate historical sharing. In addition, an individual’s moral consciousness [ 27 ] is referred to as thoughts about moral principles that vary according to the stage of moral development. Moral consciousness represents a deeply entrenched and pervasive conception of everyday life, unlike moral reasoning or judgement, which occurs in response to moral quandaries posed during measurement. From the findings, ideologically motivated political brand hatred is motivated by a desire to maintain one’s moral conscience, which amplifies the consequence of hatred by propagating bogus news.

One study explored attitudes and beliefs as one of the psychological factors [ 41 ]. According to Bryanov and Vziatysheva [ 58 ], attitudes and beliefs relate to people’s perceptions and assessments of themselves and other people [ 58 ]. In this study, attitudes and belief factors contributing to the sharing of fake news among social media users are explored. The scientific study of cognition is the process by which a person uses reasoning to make sense of particular subjects or data, and comprehension is the process by which a person interprets ideas and reasoning correctly [ 12 ]. With so much content available on social media, users could find it difficult to decide which information is closest to the original source. This may have an impact on their attitudes and beliefs towards sharing certain information they have come across on social media [ 12 ]. The problem of people being unable to distinguish between real and fake news has been highlighted by numerous publications. There is a lower likelihood that social media users will research the content they read or post. Any unconfirmed content can therefore be swiftly shared and distributed over social media platforms [ 59 ].

Social media users’ mindless forwarding of erroneous information is one of the main factors contributing to the spread of inaccurate information [ 60 ]. The spread of false information is frequently the result of careless people who are unaware that certain websites imitate real websites [ 59 ]. These phony websites are designed to resemble the authentic ones, yet their content is entirely fabricated. Without verifying the facts and sources, social media users are more inclined to spread content with an attention-grabbing headline [ 61 ]. False information spreads because users don’t check the content on social media sites. Social media users frequently distribute content without checking its accuracy or source [ 59 ].

Emotion is also one of the psychological factors to be studied for its influence on fake news dissemination [ 30 , 33 , 40 ]. Different emotions have been proposed to influence judgement in general and perceptions of fake news sharing. This study has shown how emotions and personal attitudes interact to make people believe fake news more and feel more motivated to spread it online for self-expression [ 62 ]. Ali et al. [ 30 ] studied anger and fear, while Anu and Fransesca [ 34 ] explored different types of emotions such as rage, happiness, despair, contempt, fear, surprise, anticipation, and trust. Chuai & Zhao explored anger emotion with sharing fake news [ 40 ]. Emotions such as anger, fear, sadness, and anxiety make social media users share bogus news for amusement or enjoyment [ 49 ]. Emotion influences the user’s proclivity to interpret bogus news as true ( 16 , 63 – 64 ). This suggests that the tendency of social media users to perceive news based on their emotions leads to them believing and sharing fake news with others [ 16 ].

Social processes

Socialization plays a key role in spreading false information. The frequency of social connection between people is the most important factor in determining socialization pleasure. The pleasure of socializing was investigated in relation to the desire to create social capital and contrast it with others while sharing news on social media ( 18 – 19 , 39 ). Social media literacy is also another psychological factor that affects fake news sharing [ 28 ]. According to Schreurs and Vandenbosch, the technical and mental skills required for users to use social media effectively and efficiently for online communication and social participation are known as social media literacy skills [ 65 ]. In addition, entertainment is another factor, which contributes to fake news sharing. Entertainment is defined as the use of social media sites to simply eliminate boredom [ 17 ]. According to Chuai and Zhao, one of the most important motives for using social media is recognition and the fulfilment of time passes is intimately tied to the dissemination of false news [ 40 ]. Furthermore, disseminating fake news for the sake of entertainment when someone else is duped by it, or just because it’s an exciting experience. These people, who might know better, might tell others who are less wary about this “funny” news to provide them with a quick thrill.

Social media platforms are being used to spread fake news to mislead the public for political ends [ 12 ]. Numerous papers have asserted that people who use social media are more likely to look for information from people who hold similar opinions to them [ 12 , 66 ]. An individual must socialize to modify their behaviour to fit within a certain social group [ 67 ]. Users of social media may submit facts to gain social acceptance and enhance their image out of a desire to better themselves, making it difficult for them to distinguish between accurate and false information [ 36 ]. Research indicates that messages pertaining to particular people or influencers on social media platforms like Twitter are amplified [ 68 ]. The ratings of significant users connected to the information determine the flow of information [ 60 ].

The effect and dissemination of many types of information are enhanced by social media users’ influence on their peers [ 68 ]. The information’s impact may be amplified by these influencers’ degree of power [ 69 ]. People may share information based on the thoughts and deeds of others because there is a dearth of pertinent material in online forums [ 60 ]. Certain studies indicate that people on social media will look for or spread information that supports their beliefs or worldviews [ 70 ]. One essential aspect of using social media is social media literacy, which is the capacity of users to distinguish between what is real and what is fake [ 71 ]. People are more likely to share information on social media when the contact is successful [ 36 ]. Social media users provide seemingly reasonable arguments to confirm the veracity of the content being offered [ 72 ]. Some people’s lack of intelligence is used by those who post inaccurate content and produce fake news websites [ 73 ]. Expert content judgement is required for social media users to determine if the material they get is authentic or incorrect [ 74 ].

This research contributes to the Uses and Gratifications Theory (UGT) and Self-Determination Theory (SDT) by providing insights into the psychological and motivational drivers behind the creation and dissemination of fake news on social media. UGT, which posits that individuals actively seek media to fulfil specific needs such as information, social interaction, and identity reinforcement, can explain how users engage with fake news to gratify personal or social desires like gaining social approval or validating pre-existing beliefs. In parallel, SDT, which emphasizes the role of intrinsic motivation such as autonomy, competence, and relatedness which helps to explain how individuals might create or spread fake news as a way to assert control (autonomy), demonstrate knowledge (competence), or foster social connections (relatedness). Through these lenses, this research sheds light on the complex interplay of personal, social, and psychological factors that drive fake news behaviour, thus extending the theoretical understanding of media consumption and interaction in digital environments.

The Uses and Gratifications theory [ 75 ] is a well-liked theory that is frequently used to investigate social media gratifications. It postulates that people utilise technologies to satisfy their psychological and social needs. The theory focuses on what individuals do with media instead of what media does to them and was applied as an extension of requirements and motivation theory, despite its initial design and application to comprehend the factors influencing users’ choice of media [ 76 ]. The Uses and Gratifications theory can be broadly classified into four categories which are social, process, content, and technology. Self Determination Theory evaluates human motivation and personality and asserts that people have basic psychological needs to be met, such as relatedness, autonomy, and competence [ 77 ]. Individuals grow, feel good, and maintain their integrity when these basic needs are met. Self Determination Theory contributes to the explanation of how the need for relatedness and a sense of belonging drives the millennial generation’s version of fear of missing out. It also involves the fear of losing out on enjoyable and fulfilling experiences [ 78 ]. There is evidence that connects fear of missing out to unhealthy habits such as excessive Internet use [ 79 , 80 ] and dissemination of fake news [ 36 ].

Theoretical and practical implications

The findings of this systematic review contribute to the development of a more comprehensive theoretical framework for understanding the psychological factors involved in the creation and dissemination of fake news on social media. By integrating cognitive, emotional, and social identity theories, this review extends existing research beyond isolated psychological processes to illustrate how these factors interact in complex ways. For instance, cognitive biases such as confirmation bias and the illusory truth effect, previously studied in isolation, are shown to work in tandem with emotional triggers and social identity-driven motivations to shape user behaviour. The review also has implications for social identity theory [ 81 ] which explains how individuals’ identification with particular social or political groups influences their information-sharing behaviour.

Weeks & Gil de Zúñiga [ 82 ] show that partisanship and group loyalty can increase the likelihood of sharing misinformation that aligns with one’s social identity, even when it is false. The review integrates these insights with social psychology theories of group polarization [ 83 ] suggesting that in-group dynamics and the need for social approval play key roles in the propagation of fake news. This more holistic approach highlights the importance of considering multi-level influences, advancing theories of media psychology, misinformation, and social identity in digital contexts. Additionally, by advancing these theoretical insights, this review encourages future research to adopt interdisciplinary approaches that incorporate psychological, communication, and sociological perspectives, thus filling gaps in existing theoretical models on the psychological underpinnings of fake news dissemination.

In addition, by incorporating Uses and Gratifications Theory (UGT) and Self-Determination Theory (SDT) into this review highlights the psychological motivations behind fake news creation and dissemination, emphasizing that these behaviours are often rooted in deeper psychological needs for gratification, autonomy, and social belonging. This offers a more nuanced understanding of fake news engagement, suggesting that interventions should not only focus on debunking false information but also address the underlying psychological drivers that compel individuals to create and share misinformation. These theories also encourage future research to explore how satisfying these psychological needs might lead to a preference for emotionally charged, biased, or sensational content, further contributing to the persistence and spread of fake news.

From a practical standpoint, the insights from this review can inform strategies for mitigating the spread of fake news on social media. For social media platforms and policymakers, understanding the psychological drivers of misinformation dissemination can guide the design of more effective interventions, such as content moderation tools, educational campaigns, and user prompts that target specific cognitive biases or emotional responses. For example, increasing users’ awareness of cognitive biases could lead to more critical engagement with content, while strategies aimed at reducing emotional contagion, such as flagging highly emotive posts, may curb rapid misinformation sharing. Additionally, this review’s findings can help media literacy programs focus on building resilience against the psychological triggers that make users vulnerable to fake news, thereby empowering individuals to make more informed decisions online. Finally, insights into social identity dynamics can be used to develop community-based interventions that address group-based misinformation sharing, fostering healthier online environments.

Limitation and future studies

The conclusions drawn from the findings of this systematic study should be evaluated in light of the numerous limitations. Firstly, the 23 examined research used different types of research design. As a result, there is a possibility of bias in our perception, magnitude of impacts, and research lacks consistency. Furthermore, this review explored various types of psychological factors, alongside other significant construct, which can heavily influence the dissemination of fake news. Future reviews should consider focusing on a single psychological factor for emphasis. The main source of secondary data for our analysis is academic literature on misleading news. There aren’t many sources for the grey literature, despite our best efforts to incorporate it in our review. Future studies can therefore build on this work and produce a deeper understanding of the topic at hand. Besides that, to meet our research goals and provide support for our critical review effort, we have used significant databases and meaningful keywords to find pertinent papers.

We present our work as wholly unique, comprehensive, and important in nature given our keyword strategy. It serves as a launchpad for more study aimed at delving deeper into the rapidly developing field of fake news. Besides that, this review aims to focus on psychological factors in general. Future research should focus on just one factors to gain more insights on how the factors affects dissemination of fake news. Future studies should collect data on other potential factors or essential characteristics and their interactions in predicting the dissemination and creation of fake news, which would aid in developing a more specific profile in reducing this issue. Although this study focuses on creation and dissemination of fake news, but the research only able to include one study from creation of fake news where in future research equal number of studies should be included or the review should only focus on either creation or dissemination for a more comprehensive finding.

Besides that, future studies could investigate how specific psychological factors, such as cognitive dissonance, emotional contagion, or social identity theory, influence not only the reception but also the active creation of misinformation. Additionally, research could delve into how psychological characteristics differ across populations or cultural contexts in shaping susceptibility to fake news. There is also a need to explore the psychological impact of repeated exposure to misinformation on behaviour and belief systems, as well as how interventions targeting these factors might mitigate the spread of fake news. Lastly, interdisciplinary research combining psychological insights with technological solutions could offer a more holistic approach to addressing the issue. By suggesting these avenues for future inquiry, the authors would significantly contribute to advancing both the theoretical and practical understanding of the psychological dimensions driving the creation and dissemination of fake news.

Researchers concur that due to substantial personal and community costs, it should become a new public health priority on the impact of disseminating and creating fake news. This comprehensive review found that different types of psychological factors may play a significant effect in the dissemination and creation of fake news. Lastly, this review educates individuals on the significance of false information. Specifically, it will help individuals comprehend the effects that false information on social media can have on their lives. Furthermore, this research will assist policymakers in creating strategies to combat false news by offering a more comprehensive knowledge of the phenomena. The results of this study could help anyone making decisions during a serious emergency. It may be more successful to concentrate on the psychological factors rather than the emotional component of information control when attempting to curb the dissemination of rumours, particularly those that have the potential to significantly alter people’s actions. To put it another way, changing people’s attitudes and beliefs regarding rumours may prove to be far more significant than emphasising the feelings of individuals who hear about rumours and may end up distributing them to a large number of people.

Data availability

No datasets were generated or analysed during the current study.

Shin J, Jian L, Driscoll K, Bar F. The diffusion of misinformation on social media: temporal pattern, message, and source. Comput Hum Behav. 2018;83:278–87. https://doi.org/10.1016/j.chb.2018.02.008 .

Article   Google Scholar  

Guo L, Zhang Y. Information flow within and across online media platforms: an agenda-setting analysis of rumor diffusion on news websites, Weibo, and WeChat in China. Journalism Stud. 2020;21(15):2176–95. https://doi.org/10.1080/1461670X.2020.1827012 .

Lazer DM, Baum MA, Benkler Y, Berinsky AJ, Greenhill KM, Menczer F, Metzger MJ, Nyhan B, Pennycook G, Rothschild D, Schudson M. The science of fake news. Science. 2018;359(6380):1094–6. https://doi.org/10.1371/journal.pone.0239666 .

Article   PubMed   Google Scholar  

Ruchansky N, Seo S, Liu Y, Csi. A hybrid deep model for fake news detection. InProceedings of the 2017 ACM on Conference on Information and Knowledge Management 2017 (pp. 797–806). https://doi.org/10.1145/3132847.3132877

Pennycook G, Rand DG. The Implied Truth Effect: attaching warnings to a subset of fake news stories increases perceived accuracy of stories without warnings. Manage Sci. 2018;66(11):4944–57. https://doi.org/10.1287/mnsc.2019.3478 .

Lewandowsky S, Ecker UKH, Cook J. Beyond misinformation: understanding and coping with the Post-truth era. J Appl Res Memory Cognition. 2017;6(4):353–69. https://doi.org/10.1016/j.jarmac.2017.07.008 .

Faustini PH, Covoes TF. Fake news detection in multiple platforms and languages. Expert Syst Appl. 2020;158:113503. https://doi.org/10.1016/j.eswa.2020.113503 .

Huang YF, Chen PH. Fake news detection using an ensemble learning model based on self-adaptive harmony search algorithms. Expert Syst Appl. 2020;159:113584. https://doi.org/10.1016/j.eswa.2020.113584 .

Shin J, Thorson K. Partisan selective sharing: the biased diffusion of fact-checking messages on social media. J Commun. 2017;67:233–55. https://doi.org/10.1111/jcom.12284 .

Jang SM, Geng T, Queenie Li J-Y, Xia R, Huang C-T, Kim H, et al. A computational approach for examining the roots and spreading patterns of fake news: evolution tree analysis. Comput Hum Behav. 2018;84:103–13. https://doi.org/10.1016/j.chb.2018.02.032 .

McGrew S, Breakstone J, Ortega T, Smith M, Wineburg S. Can students evaluate online sources? Learning from assessments of civic online reasoning. Theor Res Soc Educ. 2018;46:165–93. https://doi.org/10.1080/00933104.2017.1416320 .

Weeks BE. Emotions, partisanship, and misperceptions: how anger and anxiety moderate the effect of partisan bias on susceptibility to political misinformation. J Communication. 2015;65(4):699–719. https://doi.org/10.1111/jcom.12164 .

Thomas K, Nilsson E, Festin K, Henriksson P, Lowén M, Löf M, Kristenson M. Associations of psychosocial factors with multiple health behaviors: a population-based study of middle-aged men and women. Int J Environ Res Public Health. 2020;17(4):1239. https://doi.org/10.3390/ijerph17041239 .

Article   PubMed   PubMed Central   Google Scholar  

Rocha YM, de Moura GA, Desidério GA, de Oliveira CH, Lourenço FD, de Figueiredo Nicolete LD. The impact of fake news on social media and its influence on health during the COVID-19 pandemic: a systematic review. J Public Health 2021 Oct 9:1–0. https://doi.org/10.1007/s10389-021-01658-z

Friggeri A, Adamic LA, Eckles D, Cheng J. (2014). Rumor Cascades. Proceedings of the Eighth International AAAI Conference on Weblogs and Social Media (ICWSM), 101–110. Retrieved from https://www.aaai.org

Martel C, Pennycook G, Rand DG. Reliance on emotion promotes belief in fake news. Cogn Research: Principles Implications. 2020;5:1–20. https://doi.org/10.1186/s41235-020-00252-3 .

Balakrishnan V, Ng KS, Rahim HA. To share or not to share–the underlying motives of sharing fake news amidst the COVID-19 pandemic in Malaysia. Technol Soc. 2021;66:101676. https://doi.org/10.1016/j.techsoc.2021.101676 .

Apuke OD, Omar B. Fake news and COVID-19: modelling the predictors of fake news sharing among social media users. Telematics Inform. 2021;56:101475. https://doi.org/10.1016/j.tele.2020.101475 .

Omar B, Apuke OD, Nor ZM. The intrinsic and extrinsic factors predicting fake news sharing among social media users: the moderating role of fake news awareness. Curr Psychol. 2024;43(2):1235–47. https://doi.org/10.1007/s12144-023-04343-4 .

Moravec PL, Minas RA, Dennis AR. Fake news on social media: people believe what they want to believe when it makes no sense at all. MIS Q. 2019;43(4):1343–60.

Bode L, Vraga EK. See something, say something: correction of global health misinformation on social media. Health Commun. 2018;33(9):1131–40.

Oeldorf-Hirsch A, Sundar SS. Posting, commenting, and tagging: effects of sharing news stories on Facebook. Comput Hum Behav. 2015;44:240–9.

Khan A, Brohman K, Addas S. The anatomy of ‘fake news’: studying false messages as digital objects. J Inform Technol. 2022;37(2):122–43. https://doi.org/10.1177/02683962211037693 .

Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE, Chou R. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372. https://doi.org/10.1186/s13643-021-01626-4 .

Buchanan T. Why do people spread false information online? The effects of message and viewer characteristics on self-reported likelihood of sharing social media disinformation. PLoS ONE. 2020;15(10):e0239666. https://doi.org/10.1371/journal.pone.0239666 .

Rodrigo P, Arakpogun EO, Vu MC, Olan F, Djafarova E. Can you be mindful? The effectiveness of mindfulness-driven interventions in enhancing the digital resilience to fake news on COVID-19. Inform Syst Front 2022 Mar 2:1–21. https://doi.org/10.1007/s10796-022-10258-5

Sharma I, Jain K, Behl A, Baabdullah A, Giannakis M, Dwivedi Y. Examining the motivations of sharing political deepfake videos: the role of political brand hate and moral consciousness. Internet Res. 2023;33(5):1727–49. https://doi.org/10.1108/INTR-07-2022-0563 .

Wei L, Gong J, Xu J, Abidin NE, Apuke OD. Do social media literacy skills help in combating fake news spread? Modelling the moderating role of social media literacy skills in the relationship between rational choice factors and fake news sharing behaviour. Telematics Inform. 2023;76:101910.

Apuke OD, Omar B. Modelling the antecedent factors that affect online fake news sharing on COVID-19: the moderating role of fake news knowledge. Health Educ Res. 2020;35(5):490–503.

Ali K, Li C, Muqtadir SA. The effects of emotions, individual attitudes towards vaccination, and social endorsements on perceived fake news credibility and sharing motivations. Comput Hum Behav. 2022;134:107307.

Stefanone MA, Vollmer M, Covert JM. In news we trust? Examining credibility and sharing behaviors of fake news. InProceedings of the 10th international conference on social media and society 2019 Jul 19 (pp. 136–147). https://doi.org/10.1145/3328529.3328554

Shehata A, Eldakar M. An exploration of Egyptian Facebook users’ perceptions and behavior of COVID-19 misinformation. Sci Technol Libr. 2021;40(4):390–415. https://doi.org/10.1080/0194262X.2021.1925203 .

Shrestha A, Spezzano F (2021) An analysis of people’s reasoning for sharing real and fake news. Boise State University.

Lawson MA, Kakkar H. Of pandemics, politics, and personality: the role of conscientiousness and political ideology in the sharing of fake news. J Exp Psychol Gen. 2022;151(5):1154. https://doi.org/10.1037/xge0001120 .

Lobato EJ, Powell M, Padilla LM, Holbrook C. Factors predicting willingness to share COVID-19 misinformation. Front Psychol. 2020;11:566108. https://doi.org/10.3389/fpsyg.2020.566108 .

Talwar S, Dhir A, Kaur P, Zafar N, Alrasheedy M. Why do people share fake news? Associations between the dark side of social media use and fake news sharing behavior. J Retail Consum Serv. 2019;51:72–82. https://doi.org/10.1016/j.jretconser.2019.05.026 .

Kumar A, Shankar A, Behl A, Arya V, Gupta N. Should I share it? Factors influencing fake news-sharing behaviour: a behavioural reasoning theory perspective. Technol Forecast Soc Chang. 2023;193:122647. https://doi.org/10.1016/j.techfore.2023.122647 .

Nurbakova D, Ermakova L, Ovchinnikova I. Understanding the Personality of Contributors to Information Cascades in Social Media in response to the COVID-19 Pandemic. In2020 International Conference on Data Mining Workshops (ICDMW) 2020 Nov 17 (pp. 45–52). IEEE.

Thanh NN, Tung PH, Thu NH, Kien PD, Nguyet NA. Factors affecting the share of fake news about covid-19 outbreak on social networks in Vietnam. J Lib Int Affairs. 2021;7(3):179–95. https://doi.org/10.47305/JLIA2137179t .

Chuai Y, Zhao J. Anger can make fake news viral online. Front Phys. 2022;10:970174.

Li K, Li J, Zhou F. The effects of personality traits on online rumor sharing: the mediating role of fear of COVID-19. Int J Environ Res Public Health. 2022;19:6157. https://doi.org/10.3390/ijerph19106157 .

Weimann-Saks D, Elshar-Malka V, Ariel Y, Weimann G. Spreading online rumours during the Covid-19 pandemic: the role of users’ knowledge, trust and emotions as predictors of the spreading patterns. J Int Communication. 2022;28(2):249–64. https://doi.org/10.1080/13216597.2022.2099443 .

Han J, Cha M, Lee W. Emotion and misinformation in times of public health crisis. Harv Kennedy School (HKS) Misinformation Rev. 2020;1(3).

Chen X, Sin SC, Theng YL, Lee CS. Why students share misinformation on social media: motivation, gender, and study-level differences. J Acad Librariansh. 2015;41(5):583–92. https://doi.org/10.1016/j.acalib.2015.07.003 .

Fehr E, Fischbacher U. The nature of human altruism. Nature. 2003;425(6960):785–91.

Plume CJ, Slade EL. Sharing of Sponsored Advertisements on Social Media: a uses and gratifications perspective. Inform Syst Front. 2018;20(3):471–83. https://doi.org/10.1007/s10796-017-9821-8 .

Ma WWK, Chan A. Knowledge sharing and social media: Altruism, perceived online attachment motivation, and perceived online relationship commitment. Comput Hum Behav. 2014;39:51–8. https://doi.org/10.1016/j.chb.2014.06.015 .

Ryan RM, Deci EL. Intrinsic and extrinsic motivations: classic definitions and new directions. Contemp Educ Psychol. 2000;25(1):54–67. https://doi.org/10.1007/s10964-007-9178-2 .

Islam AN, Laato S, Talukder S, Sutinen E. Misinformation sharing and social media fatigue during COVID-19: an affordance and cognitive load perspective. Technol Forecast Soc Chang. 2020;159:120201.

Baumeister RF, DeWall CN, Ciarocco NJ, Twenge JM. Social exclusion impairs self-regulation. J Personal Soc Psychol. 2005;88(4):589.

Logan K, Bright LF, Grau SL. Unfriend me, please! Social media fatigue and the theory of rational choice. J Mark Theory Pract. 2018;26(4):357–67.

Vosoughi S, Roy D, Aral S. The spread of true and false news online. Science. 2018;359(6380):1146–51.

Cinelli M, Morales GDF, Galeazzi A, Quattrociocchi W, Starnini M. (2021). The echo chamber effect on social media. Proceedings of the National Academy of Sciences, 118(9), e2023301118.

Pennycook G, Rand DG. The Implied Truth Effect: attaching warnings to a subset of fake news stories increases perceived accuracy of stories without warnings. Manage Sci. 2019;66(11):4944–57.

Costa PT, McCrae RR. A five-factor theory of personality. The five-factor model of personality. Theoretical Perspect. 1999;2:51–87.

Google Scholar  

Lee K, Mahmud J, Chen J, Zhou M, Nichols J. Who will retweet this? automatically identifying and engaging strangers on twitter to spread information. InProceedings of the 19th international conference on Intelligent User Interfaces 2014 Feb 24 (pp. 247–256). https://doi.org/10.1145/2557500.2557502

Mahmud J, Zhou M, Megiddo N, Nichols J, Drews C. Optimizing the selection of strangers to answer questions in social media. arXiv preprint arXiv:1404.2013. 2014 Apr 8.

Bryanov K, Vziatysheva V. Determinants of individuals’ belief in fake news: a scoping review determinants of belief in fake news. PLoS ONE. 2021;16(6):e0253717. https://doi.org/10.1371/journal.pone.0253717 .

Bondielli A, Marcelloni F. A survey on fake news and rumour detection techniques. Inf Sci. 2019;497:38–55. https://doi.org/10.1016/j.ins.2019.05.035 .

Wang Q, Yang X, Xi W. Effects of group arguments on rumor belief and transmission in online communities: an information cascade and group polarization perspective. Inf Manag. 2018;55(4):441–9. https://doi.org/10.1016/j.im.2017.10.004 .

Rochlin N. Fake news: belief in post-truth. Libr Hi Tech. 2017;35(3):386–92. doi: 0.1108/LHT-03-2017-0062.

Weeks BE. Emotions, partisanship, and misperceptions: how anger and anxiety moderate the effect of partisan bias on susceptibility to political misinformation. J Communication. 2015;65(4):699–719.

Germani F, Biller-Andorno N. The anti-vaccination infodemic on social media: a behavioral analysis. PLoS ONE. 2021;16(3):e0247642. https://doi.org/10.1371/journal.pone.0247642 .

Sudhir S, Unnithan AB. Marketplace rumor sharing among young consumers: the role of anxiety and arousal. Young Consumers. 2019;20(1):1–3. https://doi.org/10.1108/YC-05-2018-00809 .

Schreurs L, Vandenbosch L. Introducing the Social Media Literacy (SMILE) model with the case of the positivity bias on social media. J Child Media. 2021;15(3):320–37. https://doi.org/10.1080/17482798.2020.1809481 .

Buschman J. Good news, bad news, and fake news: going beyond political literacy to democracy and libraries. J Doc. 2019;75(1):213–28. https://doi.org/10.1108/JD-05-2018-0074 .

Colliander J. This is fake news’: investigating the role of conformity to other users’ views when commenting on and spreading disinformation in social media. Comput Hum Behav. 2019;97:202–15. https://doi.org/10.1016/j.chb.2019.03.032 .

Vijaykumar S, Nowak G, Himelboim I, Jin Y. Virtual Zika transmission after the first U.S. case: who said what and how it spread on Twitter. Am J Infect Control. 2018;46(5):549–57. https://doi.org/10.1016/j.ajic.2017.10.015 .

Borges-Tiago MT, Tiago F, Cosme C. Exploring users’ motivations to participate in viral communication on social media. J Bus Res. 2018;101:574–82. https://doi.org/10.1016/j.jbusres.2018.11.011 .

Lor PJ. Democracy, information, and libraries in a time of post-truth discourse. Libr Manag. 2018;39(5):307–21. https://doi.org/10.1108/LM-06-2017-0061 .

Figueira Á, Oliveira L. The current state of fake news: challenges and opportunities. Proc Comput Sci. 2017;121:817–25. https://doi.org/10.1016/j.procs.2017.11.106 .

Park K, Rim H. Social media hoaxes, political ideology, and the role of issue confidence. Telematics Inf. 2019;36:1–11. https://doi.org/10.1016/j.tele.2018.11.001 .

Burkhardt JM. History of fake news. Libr Technol Rep. 2017;53(8):5–9.

Obelitz Søe S. Algorithmic detection of misinformation and disinformation: gricean perspectives. J Doc. 2018;72(2):309.

Blumler JG, Katz E. The uses of Mass communications: current perspectives on Gratifications Research. Beverly Hills, California: Sage; 1974.

Katz E, Blumler JG, Gurevitch M. Uses and gratification research. Publ Opin Q. 1973;37:509–23. https://doi.org/10.1086/268109 .

Ryan RM, Deci EL. Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Am Psychol. 2000;55:68–78. https://doi.org/10.1037/0003-066x.55.1.68 .

Przybylski AK, Murayama K, DeHaan CR, Gladwell V. Motivational, emotional, and behavioral correlates of fear of missing out. Comput Hum Behav. 2013;29:1841–8. https://doi.org/10.1016/j.chb.2013.02.014 .

King DL, Delfabbro PH. The cognitive psychopathology of internet gaming disorder in adolescence. J Abnorm Child Psychol. 2016;44:1635–45. https://doi.org/10.1007/s10802-016-0135-y .

Dempsey AE, O’Brien KD, Tiamiyu MF, Elhai JD. Fear of missing out (FoMO) and rumination mediate relations between social anxiety and problematic Facebook use. Addict Behav Rep. 2019;9:100150. https://doi.org/10.1016/j. abrep.2018.100150.

Tajfel H, Turner JC. The social identity theory of intergroup behavior. In: Worchel S, Austin WG, editors. Psychology of intergroup relations. Chicago: Nelson-Hall; 1986. pp. 7–24.

Weeks BE, de Gil H. The role of social media in political participation: a social identity perspective. New Media Soc. 2019;21(6):1362–83. https://doi.org/10.1177/1461444818780971 .

Sunstein CR. Republic.com. Princeton University Press; 2002.

Download references

Acknowledgements

The authors appreciate all the researchers whose articles were used in the present research.

This review is based on the research supported by the Universitas Padjadjaran grant [Grant number: 1465/UN6.I/TU.00/2023] / SK-2023-017].

Author information

Authors and affiliations.

School of Psychology and Social Sciences, Department of Psychology, International Medical University, 126, Jalan Jalil Perkasa 19, Bukit Jalil, Kuala Lumpur, 57000, Malaysia

Shalini Munusamy

Centre for Research in Psychology and Human Well-Being, Faculty of Social Sciences and Humanities, Universiti Kebangsaan Malaysia, Bangi, 43600 UKM, Malaysia

Kalaivanan Syasyila, Azahah Abu Hassan Shaari & Mohammad Rahim Kamaluddin

Centre for Research in Media and Communication, Faculty of Social Sciences and Humanities, Universiti Kebangsaan Malaysia, Bangi, 43600 UKM, Malaysia

Muhammad Adnan Pitchan

Centre for Psychological Innovation and Research, Faculty of Psychology Universitas Padjadjaran, West Java, 45363, Indonesia

Ratna Jatnika

You can also search for this author in PubMed   Google Scholar

Contributions

S.M. and A.A.H.S. did the conceptualization, framework, literature search and screening of the literature under the supervision of M.R.K. and R.J. Besides that, K.S. and M.A.P. reviewed the paper.

Corresponding authors

Correspondence to Mohammad Rahim Kamaluddin or Ratna Jatnika .

Ethics declarations

Ethics approval and consent to participate.

Not applicable.

Consent for publication

Competing interests.

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ .

Reprints and permissions

About this article

Cite this article.

Munusamy, S., Syasyila, K., Shaari, A.A.H. et al. Psychological factors contributing to the creation and dissemination of fake news among social media users: a systematic review. BMC Psychol 12 , 673 (2024). https://doi.org/10.1186/s40359-024-02129-2

Download citation

Received : 23 May 2024

Accepted : 28 October 2024

Published : 18 November 2024

DOI : https://doi.org/10.1186/s40359-024-02129-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Dissemination
• Social media users
• Psychological factors

BMC Psychology

ISSN: 2050-7283

• General enquiries: [email protected]

Advertisement

Fake news, disinformation and misinformation in social media: a review

• Original Article
• Published: 09 February 2023
• Volume 13 , article number  30 , ( 2023 )

Cite this article

• Esma Aïmeur   ORCID: orcid.org/0000-0001-7414-5454 1 ,
• Sabrine Amri   ORCID: orcid.org/0000-0002-5009-4573 1 &
• Gilles Brassard   ORCID: orcid.org/0000-0002-4380-117X 1  

132k Accesses

132 Citations

248 Altmetric

49 Mentions

Explore all metrics

Online social networks (OSNs) are rapidly growing and have become a huge source of all kinds of global and local news for millions of users. However, OSNs are a double-edged sword. Although the great advantages they offer such as unlimited easy communication and instant news and information, they can also have many disadvantages and issues. One of their major challenging issues is the spread of fake news. Fake news identification is still a complex unresolved issue. Furthermore, fake news detection on OSNs presents unique characteristics and challenges that make finding a solution anything but trivial. On the other hand, artificial intelligence (AI) approaches are still incapable of overcoming this challenging problem. To make matters worse, AI techniques such as machine learning and deep learning are leveraged to deceive people by creating and disseminating fake content. Consequently, automatic fake news detection remains a huge challenge, primarily because the content is designed in a way to closely resemble the truth, and it is often hard to determine its veracity by AI alone without additional information from third parties. This work aims to provide a comprehensive and systematic review of fake news research as well as a fundamental review of existing approaches used to detect and prevent fake news from spreading via OSNs. We present the research problem and the existing challenges, discuss the state of the art in existing approaches for fake news detection, and point out the future research directions in tackling the challenges.

Similar content being viewed by others

A comprehensive review on automatic detection of fake news on social media

Propagation of Fake News on Social Media: Challenges and Opportunities

A comprehensive Benchmark for fake news detection

Explore related subjects.

• Artificial Intelligence

Avoid common mistakes on your manuscript.

1 Introduction

1.1 context and motivation.

Fake news, disinformation and misinformation have become such a scourge that Marcia McNutt, president of the National Academy of Sciences of the United States, is quoted to have said (making an implicit reference to the COVID-19 pandemic) “Misinformation is worse than an epidemic: It spreads at the speed of light throughout the globe and can prove deadly when it reinforces misplaced personal bias against all trustworthy evidence” in a joint statement of the National Academies Footnote 1 posted on July 15, 2021. Indeed, although online social networks (OSNs), also called social media, have improved the ease with which real-time information is broadcast; its popularity and its massive use have expanded the spread of fake news by increasing the speed and scope at which it can spread. Fake news may refer to the manipulation of information that can be carried out through the production of false information, or the distortion of true information. However, that does not mean that this problem is only created with social media. A long time ago, there were rumors in the traditional media that Elvis was not dead, Footnote 2 that the Earth was flat, Footnote 3 that aliens had invaded us, Footnote 4 , etc.

Therefore, social media has become nowadays a powerful source for fake news dissemination (Sharma et al. 2019 ; Shu et al. 2017 ). According to Pew Research Center’s analysis of the news use across social media platforms, in 2020, about half of American adults get news on social media at least sometimes, Footnote 5 while in 2018, only one-fifth of them say they often get news via social media. Footnote 6

Hence, fake news can have a significant impact on society as manipulated and false content is easier to generate and harder to detect (Kumar and Shah 2018 ) and as disinformation actors change their tactics (Kumar and Shah 2018 ; Micallef et al. 2020 ). In 2017, Snow predicted in the MIT Technology Review (Snow 2017 ) that most individuals in mature economies will consume more false than valid information by 2022.

Recent news on the COVID-19 pandemic, which has flooded the web and created panic in many countries, has been reported as fake. Footnote 7 For example, holding your breath for ten seconds to one minute is not a self-test for COVID-19 Footnote 8 (see Fig.  1 ). Similarly, online posts claiming to reveal various “cures” for COVID-19 such as eating boiled garlic or drinking chlorine dioxide (which is an industrial bleach), were verified Footnote 9 as fake and in some cases as dangerous and will never cure the infection.

Social media outperformed television as the major news source for young people of the UK and the USA. Footnote 10 Moreover, as it is easier to generate and disseminate news online than with traditional media or face to face, large volumes of fake news are produced online for many reasons (Shu et al. 2017 ). Furthermore, it has been reported in a previous study about the spread of online news on Twitter (Vosoughi et al. 2018 ) that the spread of false news online is six times faster than truthful content and that 70% of the users could not distinguish real from fake news (Vosoughi et al. 2018 ) due to the attraction of the novelty of the latter (Bovet and Makse 2019 ). It was determined that falsehood spreads significantly farther, faster, deeper and more broadly than the truth in all categories of information, and the effects are more pronounced for false political news than for false news about terrorism, natural disasters, science, urban legends, or financial information (Vosoughi et al. 2018 ).

Over 1 million tweets were estimated to be related to fake news by the end of the 2016 US presidential election. Footnote 11 In 2017, in Germany, a government spokesman affirmed: “We are dealing with a phenomenon of a dimension that we have not seen before,” referring to an unprecedented spread of fake news on social networks. Footnote 12 Given the strength of this new phenomenon, fake news has been chosen as the word of the year by the Macquarie dictionary both in 2016 Footnote 13 and in 2018 Footnote 14 as well as by the Collins dictionary in 2017. Footnote 15 \(^,\) Footnote 16 Since 2020, the new term “infodemic” was coined, reflecting widespread researchers’ concern (Gupta et al. 2022 ; Apuke and Omar 2021 ; Sharma et al. 2020 ; Hartley and Vu 2020 ; Micallef et al. 2020 ) about the proliferation of misinformation linked to the COVID-19 pandemic.

Fake news example about a self-test for COVID-19 source: https://cdn.factcheck.org/UploadedFiles/Screenshot031120_false.jpg , last access date: 26-12-2022

The Gartner Group’s top strategic predictions for 2018 and beyond included the need for IT leaders to quickly develop Artificial Intelligence (AI) algorithms to address counterfeit reality and fake news. Footnote 17 However, fake news identification is a complex issue. (Snow 2017 ) questioned the ability of AI to win the war against fake news. Similarly, other researchers concurred that even the best AI for spotting fake news is still ineffective. Footnote 18 Besides, recent studies have shown that the power of AI algorithms for identifying fake news is lower than its ability to create it Paschen ( 2019 ). Consequently, automatic fake news detection remains a huge challenge, primarily because the content is designed to closely resemble the truth in order to deceive users, and as a result, it is often hard to determine its veracity by AI alone. Therefore, it is crucial to consider more effective approaches to solve the problem of fake news in social media.

1.2 Contribution

The fake news problem has been addressed by researchers from various perspectives related to different topics. These topics include, but are not restricted to, social science studies , which investigate why and who falls for fake news (Altay et al. 2022 ; Batailler et al. 2022 ; Sterret et al. 2018 ; Badawy et al. 2019 ; Pennycook and Rand 2020 ; Weiss et al. 2020 ; Guadagno and Guttieri 2021 ), whom to trust and how perceptions of misinformation and disinformation relate to media trust and media consumption patterns (Hameleers et al. 2022 ), how fake news differs from personal lies (Chiu and Oh 2021 ; Escolà-Gascón 2021 ), examine how can the law regulate digital disinformation and how governments can regulate the values of social media companies that themselves regulate disinformation spread on their platforms (Marsden et al. 2020 ; Schuyler 2019 ; Vasu et al. 2018 ; Burshtein 2017 ; Waldman 2017 ; Alemanno 2018 ; Verstraete et al. 2017 ), and argue the challenges to democracy (Jungherr and Schroeder 2021 ); Behavioral interventions studies , which examine what literacy ideas mean in the age of dis/mis- and malinformation (Carmi et al. 2020 ), investigate whether media literacy helps identification of fake news (Jones-Jang et al. 2021 ) and attempt to improve people’s news literacy (Apuke et al. 2022 ; Dame Adjin-Tettey 2022 ; Hameleers 2022 ; Nagel 2022 ; Jones-Jang et al. 2021 ; Mihailidis and Viotty 2017 ; García et al. 2020 ) by encouraging people to pause to assess credibility of headlines (Fazio 2020 ), promote civic online reasoning (McGrew 2020 ; McGrew et al. 2018 ) and critical thinking (Lutzke et al. 2019 ), together with evaluations of credibility indicators (Bhuiyan et al. 2020 ; Nygren et al. 2019 ; Shao et al. 2018a ; Pennycook et al. 2020a , b ; Clayton et al. 2020 ; Ozturk et al. 2015 ; Metzger et al. 2020 ; Sherman et al. 2020 ; Nekmat 2020 ; Brashier et al. 2021 ; Chung and Kim 2021 ; Lanius et al. 2021 ); as well as social media-driven studies , which investigate the effect of signals (e.g., sources) to detect and recognize fake news (Vraga and Bode 2017 ; Jakesch et al. 2019 ; Shen et al. 2019 ; Avram et al. 2020 ; Hameleers et al. 2020 ; Dias et al. 2020 ; Nyhan et al. 2020 ; Bode and Vraga 2015 ; Tsang 2020 ; Vishwakarma et al. 2019 ; Yavary et al. 2020 ) and investigate fake and reliable news sources using complex networks analysis based on search engine optimization metric (Mazzeo and Rapisarda 2022 ).

The impacts of fake news have reached various areas and disciplines beyond online social networks and society (García et al. 2020 ) such as economics (Clarke et al. 2020 ; Kogan et al. 2019 ; Goldstein and Yang 2019 ), psychology (Roozenbeek et al. 2020a ; Van der Linden and Roozenbeek 2020 ; Roozenbeek and van der Linden 2019 ), political science (Valenzuela et al. 2022 ; Bringula et al. 2022 ; Ricard and Medeiros 2020 ; Van der Linden et al. 2020 ; Allcott and Gentzkow 2017 ; Grinberg et al. 2019 ; Guess et al. 2019 ; Baptista and Gradim 2020 ), health science (Alonso-Galbán and Alemañy-Castilla 2022 ; Desai et al. 2022 ; Apuke and Omar 2021 ; Escolà-Gascón 2021 ; Wang et al. 2019c ; Hartley and Vu 2020 ; Micallef et al. 2020 ; Pennycook et al. 2020b ; Sharma et al. 2020 ; Roozenbeek et al. 2020b ), environmental science (e.g., climate change) (Treen et al. 2020 ; Lutzke et al. 2019 ; Lewandowsky 2020 ; Maertens et al. 2020 ), etc.

Interesting research has been carried out to review and study the fake news issue in online social networks. Some focus not only on fake news, but also distinguish between fake news and rumor (Bondielli and Marcelloni 2019 ; Meel and Vishwakarma 2020 ), while others tackle the whole problem, from characterization to processing techniques (Shu et al. 2017 ; Guo et al. 2020 ; Zhou and Zafarani 2020 ). However, they mostly focus on studying approaches from a machine learning perspective (Bondielli and Marcelloni 2019 ), data mining perspective (Shu et al. 2017 ), crowd intelligence perspective (Guo et al. 2020 ), or knowledge-based perspective (Zhou and Zafarani 2020 ). Furthermore, most of these studies ignore at least one of the mentioned perspectives, and in many cases, they do not cover other existing detection approaches using methods such as blockchain and fact-checking, as well as analysis on metrics used for Search Engine Optimization (Mazzeo and Rapisarda 2022 ). However, in our work and to the best of our knowledge, we cover all the approaches used for fake news detection. Indeed, we investigate the proposed solutions from broader perspectives (i.e., the detection techniques that are used, as well as the different aspects and types of the information used).

Therefore, in this paper, we are highly motivated by the following facts. First, fake news detection on social media is still in the early age of development, and many challenging issues remain that require deeper investigation. Hence, it is necessary to discuss potential research directions that can improve fake news detection and mitigation tasks. However, the dynamic nature of fake news propagation through social networks further complicates matters (Sharma et al. 2019 ). False information can easily reach and impact a large number of users in a short time (Friggeri et al. 2014 ; Qian et al. 2018 ). Moreover, fact-checking organizations cannot keep up with the dynamics of propagation as they require human verification, which can hold back a timely and cost-effective response (Kim et al. 2018 ; Ruchansky et al. 2017 ; Shu et al. 2018a ).

Our work focuses primarily on understanding the “fake news” problem, its related challenges and root causes, and reviewing automatic fake news detection and mitigation methods in online social networks as addressed by researchers. The main contributions that differentiate us from other works are summarized below:

We present the general context from which the fake news problem emerged (i.e., online deception)

We review existing definitions of fake news, identify the terms and features most commonly used to define fake news, and categorize related works accordingly.

We propose a fake news typology classification based on the various categorizations of fake news reported in the literature.

We point out the most challenging factors preventing researchers from proposing highly effective solutions for automatic fake news detection in social media.

We highlight and classify representative studies in the domain of automatic fake news detection and mitigation on online social networks including the key methods and techniques used to generate detection models.

We discuss the key shortcomings that may inhibit the effectiveness of the proposed fake news detection methods in online social networks.

We provide recommendations that can help address these shortcomings and improve the quality of research in this domain.

The rest of this article is organized as follows. We explain the methodology with which the studied references are collected and selected in Sect.  2 . We introduce the online deception problem in Sect.  3 . We highlight the modern-day problem of fake news in Sect.  4 , followed by challenges facing fake news detection and mitigation tasks in Sect.  5 . We provide a comprehensive literature review of the most relevant scholarly works on fake news detection in Sect.  6 . We provide a critical discussion and recommendations that may fill some of the gaps we have identified, as well as a classification of the reviewed automatic fake news detection approaches, in Sect.  7 . Finally, we provide a conclusion and propose some future directions in Sect.  8 .

2 Review methodology

This section introduces the systematic review methodology on which we relied to perform our study. We start with the formulation of the research questions, which allowed us to select the relevant research literature. Then, we provide the different sources of information together with the search and inclusion/exclusion criteria we used to select the final set of papers.

2.1 Research questions formulation

The research scope, research questions, and inclusion/exclusion criteria were established following an initial evaluation of the literature and the following research questions were formulated and addressed.

RQ1: what is fake news in social media, how is it defined in the literature, what are its related concepts, and the different types of it?

RQ2: What are the existing challenges and issues related to fake news?

RQ3: What are the available techniques used to perform fake news detection in social media?

2.2 Sources of information

We broadly searched for journal and conference research articles, books, and magazines as a source of data to extract relevant articles. We used the main sources of scientific databases and digital libraries in our search, such as Google Scholar, Footnote 19 IEEE Xplore, Footnote 20 Springer Link, Footnote 21 ScienceDirect, Footnote 22 Scopus, Footnote 23 ACM Digital Library. Footnote 24 Also, we screened most of the related high-profile conferences such as WWW, SIGKDD, VLDB, ICDE and so on to find out the recent work.

2.3 Search criteria

We focused our research over a period of ten years, but we made sure that about two-thirds of the research papers that we considered were published in or after 2019. Additionally, we defined a set of keywords to search the above-mentioned scientific databases since we concentrated on reviewing the current state of the art in addition to the challenges and the future direction. The set of keywords includes the following terms: fake news, disinformation, misinformation, information disorder, social media, detection techniques, detection methods, survey, literature review.

2.4 Study selection, exclusion and inclusion criteria

To retrieve relevant research articles, based on our sources of information and search criteria, a systematic keyword-based search was carried out by posing different search queries, as shown in Table  1 .

We discovered a primary list of articles. On the obtained initial list of studies, we applied a set of inclusion/exclusion criteria presented in Table  2 to select the appropriate research papers. The inclusion and exclusion principles are applied to determine whether a study should be included or not.

After reading the abstract, we excluded some articles that did not meet our criteria. We chose the most important research to help us understand the field. We reviewed the articles completely and found only 61 research papers that discuss the definition of the term fake news and its related concepts (see Table  4 ). We used the remaining papers to understand the field, reveal the challenges, review the detection techniques, and discuss future directions.

3 A brief introduction of online deception

The Cambridge Online Dictionary defines Deception as “ the act of hiding the truth, especially to get an advantage .” Deception relies on peoples’ trust, doubt and strong emotions that may prevent them from thinking and acting clearly (Aïmeur et al. 2018 ). We also define it in previous work (Aïmeur et al. 2018 ) as the process that undermines the ability to consciously make decisions and take convenient actions, following personal values and boundaries. In other words, deception gets people to do things they would not otherwise do. In the context of online deception, several factors need to be considered: the deceiver, the purpose or aim of the deception, the social media service, the deception technique and the potential target (Aïmeur et al. 2018 ; Hage et al. 2021 ).

Researchers are working on developing new ways to protect users and prevent online deception (Aïmeur et al. 2018 ). Due to the sophistication of attacks, this is a complex task. Hence, malicious attackers are using more complex tools and strategies to deceive users. Furthermore, the way information is organized and exchanged in social media may lead to exposing OSN users to many risks (Aïmeur et al. 2013 ).

In fact, this field is one of the recent research areas that need collaborative efforts of multidisciplinary practices such as psychology, sociology, journalism, computer science as well as cyber-security and digital marketing (which are not yet well explored in the field of dis/mis/malinformation but relevant for future research). Moreover, Ismailov et al. ( 2020 ) analyzed the main causes that could be responsible for the efficiency gap between laboratory results and real-world implementations.

In this paper, it is not in our scope of work to review online deception state of the art. However, we think it is crucial to note that fake news, misinformation and disinformation are indeed parts of the larger landscape of online deception (Hage et al. 2021 ).

4 Fake news, the modern-day problem

Fake news has existed for a very long time, much before their wide circulation became facilitated by the invention of the printing press. Footnote 25 For instance, Socrates was condemned to death more than twenty-five hundred years ago under the fake news that he was guilty of impiety against the pantheon of Athens and corruption of the youth. Footnote 26 A Google Trends Analysis of the term “fake news” reveals an explosion in popularity around the time of the 2016 US presidential election. Footnote 27 Fake news detection is a problem that has recently been addressed by numerous organizations, including the European Union Footnote 28 and NATO. Footnote 29

In this section, we first overview the fake news definitions as they were provided in the literature. We identify the terms and features used in the definitions, and we classify the latter based on them. Then, we provide a fake news typology based on distinct categorizations that we propose, and we define and compare the most cited forms of one specific fake news category (i.e., the intent-based fake news category).

4.1 Definitions of fake news

“Fake news” is defined in the Collins English Dictionary as false and often sensational information disseminated under the guise of news reporting, Footnote 30 yet the term has evolved over time and has become synonymous with the spread of false information (Cooke 2017 ).

The first definition of the term fake news was provided by Allcott and Gentzkow ( 2017 ) as news articles that are intentionally and verifiably false and could mislead readers. Then, other definitions were provided in the literature, but they all agree on the authenticity of fake news to be false (i.e., being non-factual). However, they disagree on the inclusion and exclusion of some related concepts such as satire , rumors , conspiracy theories , misinformation and hoaxes from the given definition. More recently, Nakov ( 2020 ) reported that the term fake news started to mean different things to different people, and for some politicians, it even means “news that I do not like.”

Hence, there is still no agreed definition of the term “fake news.” Moreover, we can find many terms and concepts in the literature that refer to fake news (Van der Linden et al. 2020 ; Molina et al. 2021 ) (Abu Arqoub et al. 2022 ; Allen et al. 2020 ; Allcott and Gentzkow 2017 ; Shu et al. 2017 ; Sharma et al. 2019 ; Zhou and Zafarani 2020 ; Zhang and Ghorbani 2020 ; Conroy et al. 2015 ; Celliers and Hattingh 2020 ; Nakov 2020 ; Shu et al. 2020c ; Jin et al. 2016 ; Rubin et al. 2016 ; Balmas 2014 ; Brewer et al. 2013 ; Egelhofer and Lecheler 2019 ; Mustafaraj and Metaxas 2017 ; Klein and Wueller 2017 ; Potthast et al. 2017 ; Lazer et al. 2018 ; Weiss et al. 2020 ; Tandoc Jr et al. 2021 ; Guadagno and Guttieri 2021 ), disinformation (Kapantai et al. 2021 ; Shu et al. 2020a , c ; Kumar et al. 2016 ; Bhattacharjee et al. 2020 ; Marsden et al. 2020 ; Jungherr and Schroeder 2021 ; Starbird et al. 2019 ; Ireton and Posetti 2018 ), misinformation (Wu et al. 2019 ; Shu et al. 2020c ; Shao et al. 2016 , 2018b ; Pennycook and Rand 2019 ; Micallef et al. 2020 ), malinformation (Dame Adjin-Tettey 2022 ) (Carmi et al. 2020 ; Shu et al. 2020c ), false information (Kumar and Shah 2018 ; Guo et al. 2020 ; Habib et al. 2019 ), information disorder (Shu et al. 2020c ; Wardle and Derakhshan 2017 ; Wardle 2018 ; Derakhshan and Wardle 2017 ), information warfare (Guadagno and Guttieri 2021 ) and information pollution (Meel and Vishwakarma 2020 ).

There is also a remarkable amount of disagreement over the classification of the term fake news in the research literature, as well as in policy (de Cock Buning 2018 ; ERGA 2018 , 2021 ). Some consider fake news as a type of misinformation (Allen et al. 2020 ; Singh et al. 2021 ; Ha et al. 2021 ; Pennycook and Rand 2019 ; Shao et al. 2018b ; Di Domenico et al. 2021 ; Sharma et al. 2019 ; Celliers and Hattingh 2020 ; Klein and Wueller 2017 ; Potthast et al. 2017 ; Islam et al. 2020 ), others consider it as a type of disinformation (de Cock Buning 2018 ) (Bringula et al. 2022 ; Baptista and Gradim 2022 ; Tsang 2020 ; Tandoc Jr et al. 2021 ; Bastick 2021 ; Khan et al. 2019 ; Shu et al. 2017 ; Nakov 2020 ; Shu et al. 2020c ; Egelhofer and Lecheler 2019 ), while others associate the term with both disinformation and misinformation (Wu et al. 2022 ; Dame Adjin-Tettey 2022 ; Hameleers et al. 2022 ; Carmi et al. 2020 ; Allcott and Gentzkow 2017 ; Zhang and Ghorbani 2020 ; Potthast et al. 2017 ; Weiss et al. 2020 ; Tandoc Jr et al. 2021 ; Guadagno and Guttieri 2021 ). On the other hand, some prefer to differentiate fake news from both terms (ERGA 2018 ; Molina et al. 2021 ; ERGA 2021 ) (Zhou and Zafarani 2020 ; Jin et al. 2016 ; Rubin et al. 2016 ; Balmas 2014 ; Brewer et al. 2013 ).

The existing terms can be separated into two groups. The first group represents the general terms, which are information disorder , false information and fake news , each of which includes a subset of terms from the second group. The second group represents the elementary terms, which are misinformation , disinformation and malinformation . The literature agrees on the definitions of the latter group, but there is still no agreed-upon definition of the first group. In Fig.  2 , we model the relationship between the most used terms in the literature.

Modeling of the relationship between terms related to fake news

The terms most used in the literature to refer, categorize and classify fake news can be summarized and defined as shown in Table  3 , in which we capture the similarities and show the differences between the different terms based on two common key features, which are the intent and the authenticity of the news content. The intent feature refers to the intention behind the term that is used (i.e., whether or not the purpose is to mislead or cause harm), whereas the authenticity feature refers to its factual aspect. (i.e., whether the content is verifiably false or not, which we label as genuine in the second case). Some of these terms are explicitly used to refer to fake news (i.e., disinformation, misinformation and false information), while others are not (i.e., malinformation). In the comparison table, the empty dash (–) cell denotes that the classification does not apply.

In Fig.  3 , we identify the different features used in the literature to define fake news (i.e., intent, authenticity and knowledge). Hence, some definitions are based on two key features, which are authenticity and intent (i.e., news articles that are intentionally and verifiably false and could mislead readers). However, other definitions are based on either authenticity or intent. Other researchers categorize false information on the web and social media based on its intent and knowledge (i.e., when there is a single ground truth). In Table  4 , we classify the existing fake news definitions based on the used term and the used features . In the classification, the references in the cells refer to the research study in which a fake news definition was provided, while the empty dash (–) cells denote that the classification does not apply.

The features used for fake news definition

4.2 Fake news typology

Various categorizations of fake news have been provided in the literature. We can distinguish two major categories of fake news based on the studied perspective (i.e., intention or content) as shown in Fig.  4 . However, our proposed fake news typology is not about detection methods, and it is not exclusive. Hence, a given category of fake news can be described based on both perspectives (i.e., intention and content) at the same time. For instance, satire (i.e., intent-based fake news) can contain text and/or multimedia content types of data (e.g., headline, body, image, video) (i.e., content-based fake news) and so on.

Fake news typology

Most researchers classify fake news based on the intent (Collins et al. 2020 ; Bondielli and Marcelloni 2019 ; Zannettou et al. 2019 ; Kumar et al. 2016 ; Wardle 2017 ; Shu et al. 2017 ; Kumar and Shah 2018 ) (see Sect.  4.2.2 ). However, other researchers (Parikh and Atrey 2018 ; Fraga-Lamas and Fernández-Caramés 2020 ; Hasan and Salah 2019 ; Masciari et al. 2020 ; Bakdash et al. 2018 ; Elhadad et al. 2019 ; Yang et al. 2019b ) focus on the content to categorize types of fake news through distinguishing the different formats and content types of data in the news (e.g., text and/or multimedia).

Recently, another classification was proposed by Zhang and Ghorbani ( 2020 ). It is based on the combination of content and intent to categorize fake news. They distinguish physical news content and non-physical news content from fake news. Physical content consists of the carriers and format of the news, and non-physical content consists of the opinions, emotions, attitudes and sentiments that the news creators want to express.

4.2.1 Content-based fake news category

According to researchers of this category (Parikh and Atrey 2018 ; Fraga-Lamas and Fernández-Caramés 2020 ; Hasan and Salah 2019 ; Masciari et al. 2020 ; Bakdash et al. 2018 ; Elhadad et al. 2019 ; Yang et al. 2019b ), forms of fake news may include false text such as hyperlinks or embedded content; multimedia such as false videos (Demuyakor and Opata 2022 ), images (Masciari et al. 2020 ; Shen et al. 2019 ), audios (Demuyakor and Opata 2022 ) and so on. Moreover, we can also find multimodal content (Shu et al. 2020a ) that is fake news articles and posts composed of multiple types of data combined together, for example, a fabricated image along with a text related to the image (Shu et al. 2020a ). In this category of fake news forms, we can mention as examples deepfake videos (Yang et al. 2019b ) and GAN-generated fake images (Zhang et al. 2019b ), which are artificial intelligence-based machine-generated fake content that are hard for unsophisticated social network users to identify.

The effects of these forms of fake news content vary on the credibility assessment, as well as sharing intentions which influences the spread of fake news on OSNs. For instance, people with little knowledge about the issue compared to those who are strongly concerned about the key issue of fake news tend to be easier to convince that the misleading or fake news is real, especially when shared via a video modality as compared to the text or the audio modality (Demuyakor and Opata 2022 ).

4.2.2 Intent-based Fake News Category

The most often mentioned and discussed forms of fake news according to researchers in this category include but are not restricted to clickbait , hoax , rumor , satire , propaganda , framing , conspiracy theories and others. In the following subsections, we explain these types of fake news as they were defined in the literature and undertake a brief comparison between them as depicted in Table  5 . The following are the most cited forms of intent-based types of fake news, and their comparison is based on what we suspect are the most common criteria mentioned by researchers.

Clickbait refers to misleading headlines and thumbnails of content on the web (Zannettou et al. 2019 ) that tend to be fake stories with catchy headlines aimed at enticing the reader to click on a link (Collins et al. 2020 ). This type of fake news is considered to be the least severe type of false information because if a user reads/views the whole content, it is possible to distinguish if the headline and/or the thumbnail was misleading (Zannettou et al. 2019 ). However, the goal behind using clickbait is to increase the traffic to a website (Zannettou et al. 2019 ).

A hoax is a false (Zubiaga et al. 2018 ) or inaccurate (Zannettou et al. 2019 ) intentionally fabricated (Collins et al. 2020 ) news story used to masquerade the truth (Zubiaga et al. 2018 ) and is presented as factual (Zannettou et al. 2019 ) to deceive the public or audiences (Collins et al. 2020 ). This category is also known either as half-truth or factoid stories (Zannettou et al. 2019 ). Popular examples of hoaxes are stories that report the false death of celebrities (Zannettou et al. 2019 ) and public figures (Collins et al. 2020 ). Recently, hoaxes about the COVID-19 have been circulating through social media.

The term rumor refers to ambiguous or never confirmed claims (Zannettou et al. 2019 ) that are disseminated with a lack of evidence to support them (Sharma et al. 2019 ). This kind of information is widely propagated on OSNs (Zannettou et al. 2019 ). However, they are not necessarily false and may turn out to be true (Zubiaga et al. 2018 ). Rumors originate from unverified sources but may be true or false or remain unresolved (Zubiaga et al. 2018 ).

Satire refers to stories that contain a lot of irony and humor (Zannettou et al. 2019 ). It presents stories as news that might be factually incorrect, but the intent is not to deceive but rather to call out, ridicule, or to expose behavior that is shameful, corrupt, or otherwise “bad” (Golbeck et al. 2018 ). This is done with a fabricated story or by exaggerating the truth reported in mainstream media in the form of comedy (Collins et al. 2020 ). The intent behind satire seems kind of legitimate and many authors (such as Wardle (Wardle 2017 )) do include satire as a type of fake news as there is no intention to cause harm but it has the potential to mislead or fool people.

Also, Golbeck et al. ( 2018 ) mention that there is a spectrum from fake to satirical news that they found to be exploited by many fake news sites. These sites used disclaimers at the bottom of their webpages to suggest they were “satirical” even when there was nothing satirical about their articles, to protect them from accusations about being fake. The difference with a satirical form of fake news is that the authors or the host present themselves as a comedian or as an entertainer rather than a journalist informing the public (Collins et al. 2020 ). However, most audiences believed the information passed in this satirical form because the comedian usually projects news from mainstream media and frames them to suit their program (Collins et al. 2020 ).

Propaganda refers to news stories created by political entities to mislead people. It is a special instance of fabricated stories that aim to harm the interests of a particular party and, typically, has a political context (Zannettou et al. 2019 ). Propaganda was widely used during both World Wars (Collins et al. 2020 ) and during the Cold War (Zannettou et al. 2019 ). It is a consequential type of false information as it can change the course of human history (e.g., by changing the outcome of an election) (Zannettou et al. 2019 ). States are the main actors of propaganda. Recently, propaganda has been used by politicians and media organizations to support a certain position or view (Collins et al. 2020 ). Online astroturfing can be an example of the tools used for the dissemination of propaganda. It is a covert manipulation of public opinion (Peng et al. 2017 ) that aims to make it seem that many people share the same opinion about something. Astroturfing can affect different domains of interest, based on which online astroturfing can be mainly divided into political astroturfing, corporate astroturfing and astroturfing in e-commerce or online services (Mahbub et al. 2019 ). Propaganda types of fake news can be debunked with manual fact-based detection models such as the use of expert-based fact-checkers (Collins et al. 2020 ).

Framing refers to employing some aspect of reality to make content more visible, while the truth is concealed (Collins et al. 2020 ) to deceive and misguide readers. People will understand certain concepts based on the way they are coined and invented. An example of framing was provided by Collins et al. ( 2020 ): “suppose a leader X says “I will neutralize my opponent” simply meaning he will beat his opponent in a given election. Such a statement will be framed such as “leader X threatens to kill Y” and this framed statement provides a total misrepresentation of the original meaning.

Conspiracy Theories

Conspiracy theories refer to the belief that an event is the result of secret plots generated by powerful conspirators. Conspiracy belief refers to people’s adoption and belief of conspiracy theories, and it is associated with psychological, political and social factors (Douglas et al. 2019 ). Conspiracy theories are widespread in contemporary democracies (Sutton and Douglas 2020 ), and they have major consequences. For instance, lately and during the COVID-19 pandemic, conspiracy theories have been discussed from a public health perspective (Meese et al. 2020 ; Allington et al. 2020 ; Freeman et al. 2020 ).

4.2.3 Comparison Between Most Popular Intent-based Types of Fake News

Following a review of the most popular intent-based types of fake news, we compare them as shown in Table  5 based on the most common criteria mentioned by researchers in their definitions as listed below.

the intent behind the news, which refers to whether a given news type was mainly created to intentionally deceive people or not (e.g., humor, irony, entertainment, etc.);

the way that the news propagates through OSN, which determines the nature of the propagation of each type of fake news and this can be either fast or slow propagation;

the severity of the impact of the news on OSN users, which refers to whether the public has been highly impacted by the given type of fake news; the mentioned impact of each fake news type is mainly the proportion of the negative impact;

and the goal behind disseminating the news, which can be to gain popularity for a particular entity (e.g., political party), for profit (e.g., lucrative business), or other reasons such as humor and irony in the case of satire, spreading panic or anger, and manipulating the public in the case of hoaxes, made-up stories about a particular person or entity in the case of rumors, and misguiding readers in the case of framing.

However, the comparison provided in Table  5 is deduced from the studied research papers; it is our point of view, which is not based on empirical data.

We suspect that the most dangerous types of fake news are the ones with high intention to deceive the public, fast propagation through social media, high negative impact on OSN users, and complicated hidden goals and agendas. However, while the other types of fake news are less dangerous, they should not be ignored.

Moreover, it is important to highlight that the existence of the overlap in the types of fake news mentioned above has been proven, thus it is possible to observe false information that may fall within multiple categories (Zannettou et al. 2019 ). Here, we provide two examples by Zannettou et al. ( 2019 ) to better understand possible overlaps: (1) a rumor may also use clickbait techniques to increase the audience that will read the story; and (2) propaganda stories, as a special instance of a framing story.

5 Challenges related to fake news detection and mitigation

To alleviate fake news and its threats, it is crucial to first identify and understand the factors involved that continue to challenge researchers. Thus, the main question is to explore and investigate the factors that make it easier to fall for manipulated information. Despite the tremendous progress made in alleviating some of the challenges in fake news detection (Sharma et al. 2019 ; Zhou and Zafarani 2020 ; Zhang and Ghorbani 2020 ; Shu et al. 2020a ), much more work needs to be accomplished to address the problem effectively.

In this section, we discuss several open issues that have been making fake news detection in social media a challenging problem. These issues can be summarized as follows: content-based issues (i.e., deceptive content that resembles the truth very closely), contextual issues (i.e., lack of user awareness, social bots spreaders of fake content, and OSN’s dynamic natures that leads to the fast propagation), as well as the issue of existing datasets (i.e., there still no one size fits all benchmark dataset for fake news detection). These various aspects have proven (Shu et al. 2017 ) to have a great impact on the accuracy of fake news detection approaches.

5.1 Content-based issue, deceptive content

Automatic fake news detection remains a huge challenge, primarily because the content is designed in a way that it closely resembles the truth. Besides, most deceivers choose their words carefully and use their language strategically to avoid being caught. Therefore, it is often hard to determine its veracity by AI without the reliance on additional information from third parties such as fact-checkers.

Abdullah-All-Tanvir et al. ( 2020 ) reported that fake news tends to have more complicated stories and hardly ever make any references. It is more likely to contain a greater number of words that express negative emotions. This makes it so complicated that it becomes impossible for a human to manually detect the credibility of this content. Therefore, detecting fake news on social media is quite challenging. Moreover, fake news appears in multiple types and forms, which makes it hard and challenging to define a single global solution able to capture and deal with the disseminated content. Consequently, detecting false information is not a straightforward task due to its various types and forms Zannettou et al. ( 2019 ).

5.2 Contextual issues

Contextual issues are challenges that we suspect may not be related to the content of the news but rather they are inferred from the context of the online news post (i.e., humans are the weakest factor due to lack of user awareness, social bots spreaders, dynamic nature of online social platforms and fast propagation of fake news).

5.2.1 Humans are the weakest factor due to the lack of awareness

Recent statistics Footnote 31 show that the percentage of unintentional fake news spreaders (people who share fake news without the intention to mislead) over social media is five times higher than intentional spreaders. Moreover, another recent statistic Footnote 32 shows that the percentage of people who were confident about their ability to discern fact from fiction is ten times higher than those who were not confident about the truthfulness of what they are sharing. As a result, we can deduce the lack of human awareness about the ascent of fake news.

Public susceptibility and lack of user awareness (Sharma et al. 2019 ) have always been the most challenging problem when dealing with fake news and misinformation. This is a complex issue because many people believe almost everything on the Internet and the ones who are new to digital technology or have less expertise may be easily fooled (Edgerly et al. 2020 ).

Moreover, it has been widely proven (Metzger et al. 2020 ; Edgerly et al. 2020 ) that people are often motivated to support and accept information that goes with their preexisting viewpoints and beliefs, and reject information that does not fit in as well. Hence, Shu et al. ( 2017 ) illustrate an interesting correlation between fake news spread and psychological and cognitive theories. They further suggest that humans are more likely to believe information that confirms their existing views and ideological beliefs. Consequently, they deduce that humans are naturally not very good at differentiating real information from fake information.

Recent research by Giachanou et al. ( 2020 ) studies the role of personality and linguistic patterns in discriminating between fake news spreaders and fact-checkers. They classify a user as a potential fact-checker or a potential fake news spreader based on features that represent users’ personality traits and linguistic patterns used in their tweets. They show that leveraging personality traits and linguistic patterns can improve the performance in differentiating between checkers and spreaders.

Furthermore, several researchers studied the prevalence of fake news on social networks during (Allcott and Gentzkow 2017 ; Grinberg et al. 2019 ; Guess et al. 2019 ; Baptista and Gradim 2020 ) and after (Garrett and Bond 2021 ) the 2016 US presidential election and found that individuals most likely to engage with fake news sources were generally conservative-leaning, older, and highly engaged with political news.

Metzger et al. ( 2020 ) examine how individuals evaluate the credibility of biased news sources and stories. They investigate the role of both cognitive dissonance and credibility perceptions in selective exposure to attitude-consistent news information. They found that online news consumers tend to perceive attitude-consistent news stories as more accurate and more credible than attitude-inconsistent stories.

Similarly, Edgerly et al. ( 2020 ) explore the impact of news headlines on the audience’s intent to verify whether given news is true or false. They concluded that participants exhibit higher intent to verify the news only when they believe the headline to be true, which is predicted by perceived congruence with preexisting ideological tendencies.

Luo et al. ( 2022 ) evaluate the effects of endorsement cues in social media on message credibility and detection accuracy. Results showed that headlines associated with a high number of likes increased credibility, thereby enhancing detection accuracy for real news but undermining accuracy for fake news. Consequently, they highlight the urgency of empowering individuals to assess both news veracity and endorsement cues appropriately on social media.

Moreover, misinformed people are a greater problem than uninformed people (Kuklinski et al. 2000 ), because the former hold inaccurate opinions (which may concern politics, climate change, medicine) that are harder to correct. Indeed, people find it difficult to update their misinformation-based beliefs even after they have been proved to be false (Flynn et al. 2017 ). Moreover, even if a person has accepted the corrected information, his/her belief may still affect their opinion (Nyhan and Reifler 2015 ).

Falling for disinformation may also be explained by a lack of critical thinking and of the need for evidence that supports information (Vilmer et al. 2018 ; Badawy et al. 2019 ). However, it is also possible that people choose misinformation because they engage in directionally motivated reasoning (Badawy et al. 2019 ; Flynn et al. 2017 ). Online clients are normally vulnerable and will, in general, perceive web-based networking media as reliable, as reported by Abdullah-All-Tanvir et al. ( 2019 ), who propose to mechanize fake news recognition.

It is worth noting that in addition to bots causing the outpouring of the majority of the misrepresentations, specific individuals are also contributing a large share of this issue (Abdullah-All-Tanvir et al. 2019 ). Furthermore, Vosoughi et al. (Vosoughi et al. 2018 ) found that contrary to conventional wisdom, robots have accelerated the spread of real and fake news at the same rate, implying that fake news spreads more than the truth because humans, not robots, are more likely to spread it.

In this case, verified users and those with numerous followers were not necessarily responsible for spreading misinformation of the corrupted posts (Abdullah-All-Tanvir et al. 2019 ).

Viral fake news can cause much havoc to our society. Therefore, to mitigate the negative impact of fake news, it is important to analyze the factors that lead people to fall for misinformation and to further understand why people spread fake news (Cheng et al. 2020 ). Measuring the accuracy, credibility, veracity and validity of news contents can also be a key countermeasure to consider.

5.2.2 Social bots spreaders

Several authors (Shu et al. 2018b , 2017 ; Shi et al. 2019 ; Bessi and Ferrara 2016 ; Shao et al. 2018a ) have also shown that fake news is likely to be created and spread by non-human accounts with similar attributes and structure in the network, such as social bots (Ferrara et al. 2016 ). Bots (short for software robots) exist since the early days of computers. A social bot is a computer algorithm that automatically produces content and interacts with humans on social media, trying to emulate and possibly alter their behavior (Ferrara et al. 2016 ). Although they are designed to provide a useful service, they can be harmful, for example when they contribute to the spread of unverified information or rumors (Ferrara et al. 2016 ). However, it is important to note that bots are simply tools created and maintained by humans for some specific hidden agendas.

Social bots tend to connect with legitimate users instead of other bots. They try to act like a human with fewer words and fewer followers on social media. This contributes to the forwarding of fake news (Jiang et al. 2019 ). Moreover, there is a difference between bot-generated and human-written clickbait (Le et al. 2019 ).

Many researchers have addressed ways of identifying and analyzing possible sources of fake news spread in social media. Recent research by Shu et al. ( 2020a ) describes social bots use of two strategies to spread low-credibility content. First, they amplify interactions with content as soon as it is created to make it look legitimate and to facilitate its spread across social networks. Next, they try to increase public exposure to the created content and thus boost its perceived credibility by targeting influential users that are more likely to believe disinformation in the hope of getting them to “repost” the fabricated content. They further discuss the social bot detection systems taxonomy proposed by Ferrara et al. ( 2016 ) which divides bot detection methods into three classes: (1) graph-based, (2) crowdsourcing and (3) feature-based social bot detection methods.

Similarly, Shao et al. ( 2018a ) examine social bots and how they promote the spread of misinformation through millions of Twitter posts during and following the 2016 US presidential campaign. They found that social bots played a disproportionate role in spreading articles from low-credibility sources by amplifying such content in the early spreading moments and targeting users with many followers through replies and mentions to expose them to this content and induce them to share it.

Ismailov et al. ( 2020 ) assert that the techniques used to detect bots depend on the social platform and the objective. They note that a malicious bot designed to make friends with as many accounts as possible will require a different detection approach than a bot designed to repeatedly post links to malicious websites. Therefore, they identify two models for detecting malicious accounts, each using a different set of features. Social context models achieve detection by examining features related to an account’s social presence including features such as relationships to other accounts, similarities to other users’ behaviors, and a variety of graph-based features. User behavior models primarily focus on features related to an individual user’s behavior, such as frequency of activities (e.g., number of tweets or posts per time interval), patterns of activity and clickstream sequences.

Therefore, it is crucial to consider bot detection techniques to distinguish bots from normal users to better leverage user profile features to detect fake news.

However, there is also another “bot-like” strategy that aims to massively promote disinformation and fake content in social platforms, which is called bot farms or also troll farms. It is not social bots, but it is a group of organized individuals engaging in trolling or bot-like promotion of narratives in a coordinated fashion (Wardle 2018 ) hired to massively spread fake news or any other harmful content. A prominent troll farm example is the Russia-based Internet Research Agency (IRA), which disseminated inflammatory content online to influence the outcome of the 2016 U.S. presidential election. Footnote 33 As a result, Twitter suspended accounts connected to the IRA and deleted 200,000 tweets from Russian trolls (Jamieson 2020 ). Another example to mention in this category is review bombing (Moro and Birt 2022 ). Review bombing refers to coordinated groups of people massively performing the same negative actions online (e.g., dislike, negative review/comment) on an online video, game, post, product, etc., in order to reduce its aggregate review score. The review bombers can be both humans and bots coordinated in order to cause harm and mislead people by falsifying facts.

5.2.3 Dynamic nature of online social platforms and fast propagation of fake news

Sharma et al. ( 2019 ) affirm that the fast proliferation of fake news through social networks makes it hard and challenging to assess the information’s credibility on social media. Similarly, Qian et al. ( 2018 ) assert that fake news and fabricated content propagate exponentially at the early stage of its creation and can cause a significant loss in a short amount of time (Friggeri et al. 2014 ) including manipulating the outcome of political events (Liu and Wu 2018 ; Bessi and Ferrara 2016 ).

Moreover, while analyzing the way source and promoters of fake news operate over the web through multiple online platforms, Zannettou et al. ( 2019 ) discovered that false information is more likely to spread across platforms (18% appearing on multiple platforms) compared to real information (11%).

Furthermore, recently, Shu et al. ( 2020c ) attempted to understand the propagation of disinformation and fake news in social media and found that such content is produced and disseminated faster and easier through social media because of the low barriers that prevent doing so. Similarly, Shu et al. ( 2020b ) studied hierarchical propagation networks for fake news detection. They performed a comparative analysis between fake and real news from structural, temporal and linguistic perspectives. They demonstrated the potential of using these features to detect fake news and they showed their effectiveness for fake news detection as well.

Lastly, Abdullah-All-Tanvir et al. ( 2020 ) note that it is almost impossible to manually detect the sources and authenticity of fake news effectively and efficiently, due to its fast circulation in such a small amount of time. Therefore, it is crucial to note that the dynamic nature of the various online social platforms, which results in the continued rapid and exponential propagation of such fake content, remains a major challenge that requires further investigation while defining innovative solutions for fake news detection.

5.3 Datasets issue

The existing approaches lack an inclusive dataset with derived multidimensional information to detect fake news characteristics to achieve higher accuracy of machine learning classification model performance (Nyow and Chua 2019 ). These datasets are primarily dedicated to validating the machine learning model and are the ultimate frame of reference to train the model and analyze its performance. Therefore, if a researcher evaluates their model based on an unrepresentative dataset, the validity and the efficiency of the model become questionable when it comes to applying the fake news detection approach in a real-world scenario.

Moreover, several researchers (Shu et al. 2020d ; Wang et al. 2020 ; Pathak and Srihari 2019 ; Przybyla 2020 ) believe that fake news is diverse and dynamic in terms of content, topics, publishing methods and media platforms, and sophisticated linguistic styles geared to emulate true news. Consequently, training machine learning models on such sophisticated content requires large-scale annotated fake news data that are difficult to obtain (Shu et al. 2020d ).

Therefore, datasets are also a great topic to work on to enhance data quality and have better results while defining our solutions. Adversarial learning techniques (e.g., GAN, SeqGAN) can be used to provide machine-generated data that can be used to train deeper models and build robust systems to detect fake examples from the real ones. This approach can be used to counter the lack of datasets and the scarcity of data available to train models.

6 Fake news detection literature review

Fake news detection in social networks is still in the early stage of development and there are still challenging issues that need further investigation. This has become an emerging research area that is attracting huge attention.

There are various research studies on fake news detection in online social networks. Few of them have focused on the automatic detection of fake news using artificial intelligence techniques. In this section, we review the existing approaches used in automatic fake news detection, as well as the techniques that have been adopted. Then, a critical discussion built on a primary classification scheme based on a specific set of criteria is also emphasized.

6.1 Categories of fake news detection

In this section, we give an overview of most of the existing automatic fake news detection solutions adopted in the literature. A recent classification by Sharma et al. ( 2019 ) uses three categories of fake news identification methods. Each category is further divided based on the type of existing methods (i.e., content-based, feedback-based and intervention-based methods). However, a review of the literature for fake news detection in online social networks shows that the existing studies can be classified into broader categories based on two major aspects that most authors inspect and make use of to define an adequate solution. These aspects can be considered as major sources of extracted information used for fake news detection and can be summarized as follows: the content-based (i.e., related to the content of the news post) and the contextual aspect (i.e., related to the context of the news post).

Consequently, the studies we reviewed can be classified into three different categories based on the two aspects mentioned above (the third category is hybrid). As depicted in Fig.  5 , fake news detection solutions can be categorized as news content-based approaches, the social context-based approaches that can be divided into network and user-based approaches, and hybrid approaches. The latter combines both content-based and contextual approaches to define the solution.

Classification of fake news detection approaches

6.1.1 News Content-based Category

News content-based approaches are fake news detection approaches that use content information (i.e., information extracted from the content of the news post) and that focus on studying and exploiting the news content in their proposed solutions. Content refers to the body of the news, including source, headline, text and image-video, which can reflect subtle differences.

Researchers of this category rely on content-based detection cues (i.e., text and multimedia-based cues), which are features extracted from the content of the news post. Text-based cues are features extracted from the text of the news, whereas multimedia-based cues are features extracted from the images and videos attached to the news. Figure  6 summarizes the most widely used news content representation (i.e., text and multimedia/images) and detection techniques (i.e., machine learning (ML), deep Learning (DL), natural language processing (NLP), fact-checking, crowdsourcing (CDS) and blockchain (BKC)) in news content-based category of fake news detection approaches. Most of the reviewed research works based on news content for fake news detection rely on the text-based cues (Kapusta et al. 2019 ; Kaur et al. 2020 ; Vereshchaka et al. 2020 ; Ozbay and Alatas 2020 ; Wang 2017 ; Nyow and Chua 2019 ; Hosseinimotlagh and Papalexakis 2018 ; Abdullah-All-Tanvir et al. 2019 , 2020 ; Mahabub 2020 ; Bahad et al. 2019 ; Hiriyannaiah et al. 2020 ) extracted from the text of the news content including the body of the news and its headline. However, a few researchers such as Vishwakarma et al. ( 2019 ) and Amri et al. ( 2022 ) try to recognize text from the associated image.

Most researchers of this category rely on artificial intelligence (AI) techniques (such as ML, DL and NLP models) to improve performance in terms of prediction accuracy. Others use different techniques such as fact-checking, crowdsourcing and blockchain. Specifically, the AI- and ML-based approaches in this category are trying to extract features from the news content, which they use later for content analysis and training tasks. In this particular case, the extracted features are the different types of information considered to be relevant for the analysis. Feature extraction is considered as one of the best techniques to reduce data size in automatic fake news detection. This technique aims to choose a subset of features from the original set to improve classification performance (Yazdi et al. 2020 ).

Table  6 lists the distinct features and metadata, as well as the used datasets in the news content-based category of fake news detection approaches.

News content-based category: news content representation and detection techniques

6.1.2 Social Context-based Category

Unlike news content-based solutions, the social context-based approaches capture the skeptical social context of the online news (Zhang and Ghorbani 2020 ) rather than focusing on the news content. The social context-based category contains fake news detection approaches that use the contextual aspects (i.e., information related to the context of the news post). These aspects are based on social context and they offer additional information to help detect fake news. They are the surrounding data outside of the fake news article itself, where they can be an essential part of automatic fake news detection. Some useful examples of contextual information may include checking if the news itself and the source that published it are credible, checking the date of the news or the supporting resources, and checking if any other online news platforms are reporting the same or similar stories (Zhang and Ghorbani 2020 ).

Social context-based aspects can be classified into two subcategories, user-based and network-based, and they can be used for context analysis and training tasks in the case of AI- and ML-based approaches. User-based aspects refer to information captured from OSN users such as user profile information (Shu et al. 2019b ; Wang et al. 2019c ; Hamdi et al. 2020 ; Nyow and Chua 2019 ; Jiang et al. 2019 ) and user behavior (Cardaioli et al. 2020 ) such as user engagement (Uppada et al. 2022 ; Jiang et al. 2019 ; Shu et al. 2018b ; Nyow and Chua 2019 ) and response (Zhang et al. 2019a ; Qian et al. 2018 ). Meanwhile, network-based aspects refer to information captured from the properties of the social network where the fake content is shared and disseminated such as news propagation path (Liu and Wu 2018 ; Wu and Liu 2018 ) (e.g., propagation times and temporal characteristics of propagation), diffusion patterns (Shu et al. 2019a ) (e.g., number of retweets, shares), as well as user relationships (Mishra 2020 ; Hamdi et al. 2020 ; Jiang et al. 2019 ) (e.g., friendship status among users).

Figure  7 summarizes some of the most widely adopted social context representations, as well as the most used detection techniques (i.e., AI, ML, DL, fact-checking and blockchain), in the social context-based category of approaches.

Table  7 lists the distinct features and metadata, the adopted detection cues, as well as the used datasets, in the context-based category of fake news detection approaches.

Social context-based category: social context representation and detection techniques

6.1.3 Hybrid approaches

Most researchers are focusing on employing a specific method rather than a combination of both content- and context-based methods. This is because some of them (Wu and Rao 2020 ) believe that there still some challenging limitations in the traditional fusion strategies due to existing feature correlations and semantic conflicts. For this reason, some researchers focus on extracting content-based information, while others are capturing some social context-based information for their proposed approaches.

However, it has proven challenging to successfully automate fake news detection based on just a single type of feature (Ruchansky et al. 2017 ). Therefore, recent directions tend to do a mixture by using both news content-based and social context-based approaches for fake news detection.

Table  8 lists the distinct features and metadata, as well as the used datasets, in the hybrid category of fake news detection approaches.

6.2 Fake news detection techniques

Another vision for classifying automatic fake news detection is to look at techniques used in the literature. Hence, we classify the detection methods based on the techniques into three groups:

Human-based techniques: This category mainly includes the use of crowdsourcing and fact-checking techniques, which rely on human knowledge to check and validate the veracity of news content.

Artificial Intelligence-based techniques: This category includes the most used AI approaches for fake news detection in the literature. Specifically, these are the approaches in which researchers use classical ML, deep learning techniques such as convolutional neural network (CNN), recurrent neural network (RNN), as well as natural language processing (NLP).

Blockchain-based techniques: This category includes solutions using blockchain technology to detect and mitigate fake news in social media by checking source reliability and establishing the traceability of the news content.

6.2.1 Human-based Techniques

One specific research direction for fake news detection consists of using human-based techniques such as crowdsourcing (Pennycook and Rand 2019 ; Micallef et al. 2020 ) and fact-checking (Vlachos and Riedel 2014 ; Chung and Kim 2021 ; Nyhan et al. 2020 ) techniques.

These approaches can be considered as low computational requirement techniques since both rely on human knowledge and expertise for fake news detection. However, fake news identification cannot be addressed solely through human force since it demands a lot of effort in terms of time and cost, and it is ineffective in terms of preventing the fast spread of fake content.

Crowdsourcing. Crowdsourcing approaches (Kim et al. 2018 ) are based on the “wisdom of the crowds” (Collins et al. 2020 ) for fake content detection. These approaches rely on the collective contributions and crowd signals (Tschiatschek et al. 2018 ) of a group of people for the aggregation of crowd intelligence to detect fake news (Tchakounté et al. 2020 ) and to reduce the spread of misinformation on social media (Pennycook and Rand 2019 ; Micallef et al. 2020 ).

Micallef et al. ( 2020 ) highlight the role of the crowd in countering misinformation. They suspect that concerned citizens (i.e., the crowd), who use platforms where disinformation appears, can play a crucial role in spreading fact-checking information and in combating the spread of misinformation.

Recently Tchakounté et al. ( 2020 ) proposed a voting system as a new method of binary aggregation of opinions of the crowd and the knowledge of a third-party expert. The aggregator is based on majority voting on the crowd side and weighted averaging on the third-party site.

Similarly, Huffaker et al. ( 2020 ) propose a crowdsourced detection of emotionally manipulative language. They introduce an approach that transforms classification problems into a comparison task to mitigate conflation content by allowing the crowd to detect text that uses manipulative emotional language to sway users toward positions or actions. The proposed system leverages anchor comparison to distinguish between intrinsically emotional content and emotionally manipulative language.

La Barbera et al. ( 2020 ) try to understand how people perceive the truthfulness of information presented to them. They collect data from US-based crowd workers, build a dataset of crowdsourced truthfulness judgments for political statements, and compare it with expert annotation data generated by fact-checkers such as PolitiFact.

Coscia and Rossi ( 2020 ) introduce a crowdsourced flagging system that consists of online news flagging. The bipolar model of news flagging attempts to capture the main ingredients that they observe in empirical research on fake news and disinformation.

Unlike the previously mentioned researchers who focus on news content in their approaches, Pennycook and Rand ( 2019 ) focus on using crowdsourced judgments of the quality of news sources to combat social media disinformation.

Fact-Checking. The fact-checking task is commonly manually performed by journalists to verify the truthfulness of a given claim. Indeed, fact-checking features are being adopted by multiple online social network platforms. For instance, Facebook Footnote 34 started addressing false information through independent fact-checkers in 2017, followed by Google Footnote 35 the same year. Two years later, Instagram Footnote 36 followed suit. However, the usefulness of fact-checking initiatives is questioned by journalists Footnote 37 , as well as by researchers such as Andersen and Søe ( 2020 ). On the other hand, work is being conducted to boost the effectiveness of these initiatives to reduce misinformation (Chung and Kim 2021 ; Clayton et al. 2020 ; Nyhan et al. 2020 ).

Most researchers use fact-checking websites (e.g., politifact.com, Footnote 38 snopes.com, Footnote 39 Reuters, Footnote 40 , etc.) as data sources to build their datasets and train their models. Therefore, in the following, we specifically review examples of solutions that use fact-checking (Vlachos and Riedel 2014 ) to help build datasets that can be further used in the automatic detection of fake content.

Yang et al. ( 2019a ) use PolitiFact fact-checking website as a data source to train, tune, and evaluate their model named XFake, on political data. The XFake system is an explainable fake news detector that assists end users to identify news credibility. The fakeness of news items is detected and interpreted considering both content and contextual (e.g., statements) information (e.g., speaker).

Based on the idea that fact-checkers cannot clean all data, and it must be a selection of what “matters the most” to clean while checking a claim, Sintos et al. ( 2019 ) propose a solution to help fact-checkers combat problems related to data quality (where inaccurate data lead to incorrect conclusions) and data phishing. The proposed solution is a combination of data cleaning and perturbation analysis to avoid uncertainties and errors in data and the possibility that data can be phished.

Tchechmedjiev et al. ( 2019 ) propose a system named “ClaimsKG” as a knowledge graph of fact-checked claims aiming to facilitate structured queries about their truth values, authors, dates, journalistic reviews and other kinds of metadata. “ClaimsKG” designs the relationship between vocabularies. To gather vocabularies, a semi-automated pipeline periodically gathers data from popular fact-checking websites regularly.

6.2.2 AI-based Techniques

Previous work by Yaqub et al. ( 2020 ) has shown that people lack trust in automated solutions for fake news detection However, work is already being undertaken to increase this trust, for instance by von der Weth et al. ( 2020 ).

Most researchers consider fake news detection as a classification problem and use artificial intelligence techniques, as shown in Fig.  8 . The adopted AI techniques may include machine learning ML (e.g., Naïve Bayes, logistic regression, support vector machine SVM), deep learning DL (e.g., convolutional neural networks CNN, recurrent neural networks RNN, long short-term memory LSTM) and natural language processing NLP (e.g., Count vectorizer, TF-IDF Vectorizer). Most of them combine many AI techniques in their solutions rather than relying on one specific approach.

Examples of the most widely used AI techniques for fake news detection

Many researchers are developing machine learning models in their solutions for fake news detection. Recently, deep neural network techniques are also being employed as they are generating promising results (Islam et al. 2020 ). A neural network is a massively parallel distributed processor with simple units that can store important information and make it available for use (Hiriyannaiah et al. 2020 ). Moreover, it has been proven (Cardoso Durier da Silva et al. 2019 ) that the most widely used method for automatic detection of fake news is not simply a classical machine learning technique, but rather a fusion of classical techniques coordinated by a neural network.

Some researchers define purely machine learning models (Del Vicario et al. 2019 ; Elhadad et al. 2019 ; Aswani et al. 2017 ; Hakak et al. 2021 ; Singh et al. 2021 ) in their fake news detection approaches. The more commonly used machine learning algorithms (Abdullah-All-Tanvir et al. 2019 ) for classification problems are Naïve Bayes, logistic regression and SVM.

Other researchers (Wang et al. 2019c ; Wang 2017 ; Liu and Wu 2018 ; Mishra 2020 ; Qian et al. 2018 ; Zhang et al. 2020 ; Goldani et al. 2021 ) prefer to do a mixture of different deep learning models, without combining them with classical machine learning techniques. Some even prove that deep learning techniques outperform traditional machine learning techniques (Mishra et al. 2022 ). Deep learning is one of the most widely popular research topics in machine learning. Unlike traditional machine learning approaches, which are based on manually crafted features, deep learning approaches can learn hidden representations from simpler inputs both in context and content variations (Bondielli and Marcelloni 2019 ). Moreover, traditional machine learning algorithms almost always require structured data and are designed to “learn” to act by understanding labeled data and then use it to produce new results with more datasets, which requires human intervention to “teach them” when the result is incorrect (Parrish 2018 ), while deep learning networks rely on layers of artificial neural networks (ANN) and do not require human intervention, as multilevel layers in neural networks place data in a hierarchy of different concepts, which ultimately learn from their own mistakes (Parrish 2018 ). The two most widely implemented paradigms in deep neural networks are recurrent neural networks (RNN) and convolutional neural networks (CNN).

Still other researchers (Abdullah-All-Tanvir et al. 2019 ; Kaliyar et al. 2020 ; Zhang et al. 2019a ; Deepak and Chitturi 2020 ; Shu et al. 2018a ; Wang et al. 2019c ) prefer to combine traditional machine learning and deep learning classification, models. Others combine machine learning and natural language processing techniques. A few combine deep learning models with natural language processing (Vereshchaka et al. 2020 ). Some other researchers (Kapusta et al. 2019 ; Ozbay and Alatas 2020 ; Ahmed et al. 2020 ) combine natural language processing with machine learning models. Furthermore, others (Abdullah-All-Tanvir et al. 2019 ; Kaur et al. 2020 ; Kaliyar 2018 ; Abdullah-All-Tanvir et al. 2020 ; Bahad et al. 2019 ) prefer to combine all the previously mentioned techniques (i.e., ML, DL and NLP) in their approaches.

Table  11 , which is relegated to the Appendix (after the bibliography) because of its size, shows a comparison of the fake news detection solutions that we have reviewed based on their main approaches, the methodology that was used and the models.

6.2.3 Blockchain-based Techniques for Source Reliability and Traceability

Another research direction for detecting and mitigating fake news in social media focuses on using blockchain solutions. Blockchain technology is recently attracting researchers’ attention due to the interesting features it offers. Immutability, decentralization, tamperproof, consensus, record keeping and non-repudiation of transactions are some of the key features that make blockchain technology exploitable, not just for cryptocurrencies, but also to prove the authenticity and integrity of digital assets.

However, the proposed blockchain approaches are few in number and they are fundamental and theoretical approaches. Specifically, the solutions that are currently available are still in research, prototype, and beta testing stages (DiCicco and Agarwal 2020 ; Tchechmedjiev et al. 2019 ). Furthermore, most researchers (Ochoa et al. 2019 ; Song et al. 2019 ; Shang et al. 2018 ; Qayyum et al. 2019 ; Jing and Murugesan 2018 ; Buccafurri et al. 2017 ; Chen et al. 2018 ) do not specify which fake news type they are mitigating in their studies. They mention news content in general, which is not adequate for innovative solutions. For that, serious implementations should be provided to prove the usefulness and feasibility of this newly developing research vision.

Table  9 shows a classification of the reviewed blockchain-based approaches. In the classification, we listed the following:

The type of fake news that authors are trying to mitigate, which can be multimedia-based or text-based fake news.

The techniques used for fake news mitigation, which can be either blockchain only, or blockchain combined with other techniques such as AI, Data mining, Truth-discovery, Preservation metadata, Semantic similarity, Crowdsourcing, Graph theory and SIR model (Susceptible, Infected, Recovered).

The feature that is offered as an advantage of the given solution (e.g., Reliability, Authenticity and Traceability). Reliability is the credibility and truthfulness of the news content, which consists of proving the trustworthiness of the content. Traceability aims to trace and archive the contents. Authenticity consists of checking whether the content is real and authentic.

A checkmark ( \(\checkmark \) ) in Table  9 denotes that the mentioned criterion is explicitly mentioned in the proposed solution, while the empty dash (–) cell for fake news type denotes that it depends on the case: The criterion was either not explicitly mentioned (e.g., fake news type) in the work or the classification does not apply (e.g., techniques/other).

7 Discussion

After reviewing the most relevant state of the art for automatic fake news detection, we classify them as shown in Table  10 based on the detection aspects (i.e., content-based, contextual, or hybrid aspects) and the techniques used (i.e., AI, crowdsourcing, fact-checking, blockchain or hybrid techniques). Hybrid techniques refer to solutions that simultaneously combine different techniques from previously mentioned categories (i.e., inter-hybrid methods), as well as techniques within the same class of methods (i.e., intra-hybrid methods), in order to define innovative solutions for fake news detection. A hybrid method should bring the best of both worlds. Then, we provide a discussion based on different axes.

7.1 News content-based methods

Most of the news content-based approaches consider fake news detection as a classification problem and they use AI techniques such as classical machine learning (e.g., regression, Bayesian) as well as deep learning (i.e., neural methods such as CNN and RNN). More specifically, classification of social media content is a fundamental task for social media mining, so that most existing methods regard it as a text categorization problem and mainly focus on using content features, such as words and hashtags (Wu and Liu 2018 ). The main challenge facing these approaches is how to extract features in a way to reduce the data used to train their models and what features are the most suitable for accurate results.

Researchers using such approaches are motivated by the fact that the news content is the main entity in the deception process, and it is a straightforward factor to analyze and use while looking for predictive clues of deception. However, detecting fake news only from the content of the news is not enough because the news is created in a strategic intentional way to mimic the truth (i.e., the content can be intentionally manipulated by the spreader to make it look like real news). Therefore, it is considered to be challenging, if not impossible, to identify useful features (Wu and Liu 2018 ) and consequently tell the nature of such news solely from the content.

Moreover, works that utilize only the news content for fake news detection ignore the rich information and latent user intelligence (Qian et al. 2018 ) stored in user responses toward previously disseminated articles. Therefore, the auxiliary information is deemed crucial for an effective fake news detection approach.

7.2 Social context-based methods

The context-based approaches explore the surrounding data outside of the news content, which can be an effective direction and has some advantages in areas where the content approaches based on text classification can run into issues. However, most existing studies implementing contextual methods mainly focus on additional information coming from users and network diffusion patterns. Moreover, from a technical perspective, they are limited to the use of sophisticated machine learning techniques for feature extraction, and they ignore the usefulness of results coming from techniques such as web search and crowdsourcing which may save much time and help in the early detection and identification of fake content.

7.3 Hybrid approaches

Hybrid approaches can simultaneously model different aspects of fake news such as the content-based aspects, as well as the contextual aspect based on both the OSN user and the OSN network patterns. However, these approaches are deemed more complex in terms of models (Bondielli and Marcelloni 2019 ), data availability, and the number of features. Furthermore, it remains difficult to decide which information among each category (i.e., content-based and context-based information) is most suitable and appropriate to be used to achieve accurate and precise results. Therefore, there are still very few studies belonging to this category of hybrid approaches.

7.4 Early detection

As fake news usually evolves and spreads very fast on social media, it is critical and urgent to consider early detection directions. Yet, this is a challenging task to do especially in highly dynamic platforms such as social networks. Both news content- and social context-based approaches suffer from this challenging early detection of fake news.

Although approaches that detect fake news based on content analysis face this issue less, they are still limited by the lack of information required for verification when the news is in its early stage of spread. However, approaches that detect fake news based on contextual analysis are most likely to suffer from the lack of early detection since most of them rely on information that is mostly available after the spread of fake content such as social engagement, user response, and propagation patterns. Therefore, it is crucial to consider both trusted human verification and historical data as an attempt to detect fake content during its early stage of propagation.

8 Conclusion and future directions

In this paper, we introduced the general context of the fake news problem as one of the major issues of the online deception problem in online social networks. Based on reviewing the most relevant state of the art, we summarized and classified existing definitions of fake news, as well as its related terms. We also listed various typologies and existing categorizations of fake news such as intent-based fake news including clickbait, hoax, rumor, satire, propaganda, conspiracy theories, framing as well as content-based fake news including text and multimedia-based fake news, and in the latter, we can tackle deepfake videos and GAN-generated fake images. We discussed the major challenges related to fake news detection and mitigation in social media including the deceptiveness nature of the fabricated content, the lack of human awareness in the field of fake news, the non-human spreaders issue (e.g., social bots), the dynamicity of such online platforms, which results in a fast propagation of fake content and the quality of existing datasets, which still limits the efficiency of the proposed solutions. We reviewed existing researchers’ visions regarding the automatic detection of fake news based on the adopted approaches (i.e., news content-based approaches, social context-based approaches, or hybrid approaches) and the techniques that are used (i.e., artificial intelligence-based methods; crowdsourcing, fact-checking, and blockchain-based methods; and hybrid methods), then we showed a comparative study between the reviewed works. We also provided a critical discussion of the reviewed approaches based on different axes such as the adopted aspect for fake news detection (i.e., content-based, contextual, and hybrid aspects) and the early detection perspective.

To conclude, we present the main issues for combating the fake news problem that needs to be further investigated while proposing new detection approaches. We believe that to define an efficient fake news detection approach, we need to consider the following:

Our choice of sources of information and search criteria may have introduced biases in our research. If so, it would be desirable to identify those biases and mitigate them.

News content is the fundamental source to find clues to distinguish fake from real content. However, contextual information derived from social media users and from the network can provide useful auxiliary information to increase detection accuracy. Specifically, capturing users’ characteristics and users’ behavior toward shared content can be a key task for fake news detection.

Moreover, capturing users’ historical behavior, including their emotions and/or opinions toward news content, can help in the early detection and mitigation of fake news.

Furthermore, adversarial learning techniques (e.g., GAN, SeqGAN) can be considered as a promising direction for mitigating the lack and scarcity of available datasets by providing machine-generated data that can be used to train and build robust systems to detect the fake examples from the real ones.

Lastly, analyzing how sources and promoters of fake news operate over the web through multiple online platforms is crucial; Zannettou et al. ( 2019 ) discovered that false information is more likely to spread across platforms (18% appearing on multiple platforms) compared to valid information (11%).

Availability of data and material

All the data and material are available in the papers cited in the references.

https://www.nationalacademies.org/news/2021/07/as-surgeon-general-urges-whole-of-society-effort-to-fight-health-misinformation-the-work-of-the-national-academies-helps-foster-an-evidence-based-information-environment , last access date: 26-12-2022.

https://time.com/4897819/elvis-presley-alive-conspiracy-theories/ , last access date: 26-12-2022.

https://www.therichest.com/shocking/the-evidence-15-reasons-people-think-the-earth-is-flat/ , last access date: 26-12-2022.

https://www.grunge.com/657584/the-truth-about-1952s-alien-invasion-of-washington-dc/ , last access date: 26-12-2022.

https://www.journalism.org/2021/01/12/news-use-across-social-media-platforms-in-2020/ , last access date: 26-12-2022.

https://www.pewresearch.org/fact-tank/2018/12/10/social-media-outpaces-print-newspapers-in-the-u-s-as-a-news-source/ , last access date: 26-12-2022.

https://www.buzzfeednews.com/article/janelytvynenko/coronavirus-fake-news-disinformation-rumors-hoaxes , last access date: 26-12-2022.

https://www.factcheck.org/2020/03/viral-social-media-posts-offer-false-coronavirus-tips/ , last access date: 26-12-2022.

https://www.factcheck.org/2020/02/fake-coronavirus-cures-part-2-garlic-isnt-a-cure/ , last access date: 26-12-2022.

https://www.bbc.com/news/uk-36528256 , last access date: 26-12-2022.

https://en.wikipedia.org/wiki/Pizzagate_conspiracy_theory , last access date: 26-12-2022.

https://www.theguardian.com/world/2017/jan/09/germany-investigating-spread-fake-news-online-russia-election , last access date: 26-12-2022.

https://www.macquariedictionary.com.au/resources/view/word/of/the/year/2016 , last access date: 26-12-2022.

https://www.macquariedictionary.com.au/resources/view/word/of/the/year/2018 , last access date: 26-12-2022.

https://apnews.com/article/47466c5e260149b1a23641b9e319fda6 , last access date: 26-12-2022.

https://blog.collinsdictionary.com/language-lovers/collins-2017-word-of-the-year-shortlist/ , last access date: 26-12-2022.

https://www.gartner.com/smarterwithgartner/gartner-top-strategic-predictions-for-2018-and-beyond/ , last access date: 26-12-2022.

https://www.technologyreview.com/s/612236/even-the-best-ai-for-spotting-fake-news-is-still-terrible/ , last access date: 26-12-2022.

https://scholar.google.ca/ , last access date: 26-12-2022.

https://ieeexplore.ieee.org/ , last access date: 26-12-2022.

https://link.springer.com/ , last access date: 26-12-2022.

https://www.sciencedirect.com/ , last access date: 26-12-2022.

https://www.scopus.com/ , last access date: 26-12-2022.

https://www.acm.org/digital-library , last access date: 26-12-2022.

https://www.politico.com/magazine/story/2016/12/fake-news-history-long-violent-214535 , last access date: 26-12-2022.

https://en.wikipedia.org/wiki/Trial_of_Socrates , last access date: 26-12-2022.

https://trends.google.com/trends/explore?hl=en-US &tz=-180 &date=2013-12-06+2018-01-06 &geo=US &q=fake+news &sni=3 , last access date: 26-12-2022.

https://ec.europa.eu/digital-single-market/en/tackling-online-disinformation , last access date: 26-12-2022.

https://www.nato.int/cps/en/natohq/177273.htm , last access date: 26-12-2022.

https://www.collinsdictionary.com/dictionary/english/fake-news , last access date: 26-12-2022.

https://www.statista.com/statistics/657111/fake-news-sharing-online/ , last access date: 26-12-2022.

https://www.statista.com/statistics/657090/fake-news-recogition-confidence/ , last access date: 26-12-2022.

https://www.nbcnews.com/tech/social-media/now-available-more-200-000-deleted-russian-troll-tweets-n844731 , last access date: 26-12-2022.

https://www.theguardian.com/technology/2017/mar/22/facebook-fact-checking-tool-fake-news , last access date: 26-12-2022.

https://www.theguardian.com/technology/2017/apr/07/google-to-display-fact-checking-labels-to-show-if-news-is-true-or-false , last access date: 26-12-2022.

https://about.instagram.com/blog/announcements/combatting-misinformation-on-instagram , last access date: 26-12-2022.

https://www.wired.com/story/instagram-fact-checks-who-will-do-checking/ , last access date: 26-12-2022.

https://www.politifact.com/ , last access date: 26-12-2022.

https://www.snopes.com/ , last access date: 26-12-2022.

https://www.reutersagency.com/en/ , last access date: 26-12-2022.

Abdullah-All-Tanvir, Mahir EM, Akhter S, Huq MR (2019) Detecting fake news using machine learning and deep learning algorithms. In: 7th international conference on smart computing and communications (ICSCC), IEEE, pp 1–5 https://doi.org/10.1109/ICSCC.2019.8843612

Abdullah-All-Tanvir, Mahir EM, Huda SMA, Barua S (2020) A hybrid approach for identifying authentic news using deep learning methods on popular Twitter threads. In: International conference on artificial intelligence and signal processing (AISP), IEEE, pp 1–6 https://doi.org/10.1109/AISP48273.2020.9073583

Abu Arqoub O, Abdulateef Elega A, Efe Özad B, Dwikat H, Adedamola Oloyede F (2022) Mapping the scholarship of fake news research: a systematic review. J Pract 16(1):56–86. https://doi.org/10.1080/17512786.2020.1805791

Article   Google Scholar  

Ahmed S, Hinkelmann K, Corradini F (2020) Development of fake news model using machine learning through natural language processing. Int J Comput Inf Eng 14(12):454–460

Google Scholar  

Aïmeur E, Brassard G, Rioux J (2013) Data privacy: an end-user perspective. Int J Comput Netw Commun Secur 1(6):237–250

Aïmeur E, Hage H, Amri S (2018) The scourge of online deception in social networks. In: 2018 international conference on computational science and computational intelligence (CSCI), IEEE, pp 1266–1271 https://doi.org/10.1109/CSCI46756.2018.00244

Alemanno A (2018) How to counter fake news? A taxonomy of anti-fake news approaches. Eur J Risk Regul 9(1):1–5. https://doi.org/10.1017/err.2018.12

Allcott H, Gentzkow M (2017) Social media and fake news in the 2016 election. J Econ Perspect 31(2):211–36. https://doi.org/10.1257/jep.31.2.211

Allen J, Howland B, Mobius M, Rothschild D, Watts DJ (2020) Evaluating the fake news problem at the scale of the information ecosystem. Sci Adv. https://doi.org/10.1126/sciadv.aay3539

Allington D, Duffy B, Wessely S, Dhavan N, Rubin J (2020) Health-protective behaviour, social media usage and conspiracy belief during the Covid-19 public health emergency. Psychol Med. https://doi.org/10.1017/S003329172000224X

Alonso-Galbán P, Alemañy-Castilla C (2022) Curbing misinformation and disinformation in the Covid-19 era: a view from cuba. MEDICC Rev 22:45–46 https://doi.org/10.37757/MR2020.V22.N2.12

Altay S, Hacquin AS, Mercier H (2022) Why do so few people share fake news? It hurts their reputation. New Media Soc 24(6):1303–1324. https://doi.org/10.1177/1461444820969893

Amri S, Sallami D, Aïmeur E (2022) Exmulf: an explainable multimodal content-based fake news detection system. In: International symposium on foundations and practice of security. Springer, Berlin, pp 177–187. https://doi.org/10.1109/IJCNN48605.2020.9206973

Andersen J, Søe SO (2020) Communicative actions we live by: the problem with fact-checking, tagging or flagging fake news-the case of Facebook. Eur J Commun 35(2):126–139. https://doi.org/10.1177/0267323119894489

Apuke OD, Omar B (2021) Fake news and Covid-19: modelling the predictors of fake news sharing among social media users. Telematics Inform 56:101475. https://doi.org/10.1016/j.tele.2020.101475

Apuke OD, Omar B, Tunca EA, Gever CV (2022) The effect of visual multimedia instructions against fake news spread: a quasi-experimental study with Nigerian students. J Librariansh Inf Sci. https://doi.org/10.1177/09610006221096477

Aswani R, Ghrera S, Kar AK, Chandra S (2017) Identifying buzz in social media: a hybrid approach using artificial bee colony and k-nearest neighbors for outlier detection. Soc Netw Anal Min 7(1):1–10. https://doi.org/10.1007/s13278-017-0461-2

Avram M, Micallef N, Patil S, Menczer F (2020) Exposure to social engagement metrics increases vulnerability to misinformation. arXiv preprint arxiv:2005.04682 , https://doi.org/10.37016/mr-2020-033

Badawy A, Lerman K, Ferrara E (2019) Who falls for online political manipulation? In: Companion proceedings of the 2019 world wide web conference, pp 162–168 https://doi.org/10.1145/3308560.3316494

Bahad P, Saxena P, Kamal R (2019) Fake news detection using bi-directional LSTM-recurrent neural network. Procedia Comput Sci 165:74–82. https://doi.org/10.1016/j.procs.2020.01.072

Bakdash J, Sample C, Rankin M, Kantarcioglu M, Holmes J, Kase S, Zaroukian E, Szymanski B (2018) The future of deception: machine-generated and manipulated images, video, and audio? In: 2018 international workshop on social sensing (SocialSens), IEEE, pp 2–2 https://doi.org/10.1109/SocialSens.2018.00009

Balmas M (2014) When fake news becomes real: combined exposure to multiple news sources and political attitudes of inefficacy, alienation, and cynicism. Commun Res 41(3):430–454. https://doi.org/10.1177/0093650212453600

Baptista JP, Gradim A (2020) Understanding fake news consumption: a review. Soc Sci. https://doi.org/10.3390/socsci9100185

Baptista JP, Gradim A (2022) A working definition of fake news. Encyclopedia 2(1):632–645. https://doi.org/10.3390/encyclopedia2010043

Bastick Z (2021) Would you notice if fake news changed your behavior? An experiment on the unconscious effects of disinformation. Comput Hum Behav 116:106633. https://doi.org/10.1016/j.chb.2020.106633

Batailler C, Brannon SM, Teas PE, Gawronski B (2022) A signal detection approach to understanding the identification of fake news. Perspect Psychol Sci 17(1):78–98. https://doi.org/10.1177/1745691620986135

Bessi A, Ferrara E (2016) Social bots distort the 2016 US presidential election online discussion. First Monday 21(11-7). https://doi.org/10.5210/fm.v21i11.7090

Bhattacharjee A, Shu K, Gao M, Liu H (2020) Disinformation in the online information ecosystem: detection, mitigation and challenges. arXiv preprint arXiv:2010.09113

Bhuiyan MM, Zhang AX, Sehat CM, Mitra T (2020) Investigating differences in crowdsourced news credibility assessment: raters, tasks, and expert criteria. Proc ACM Hum Comput Interact 4(CSCW2):1–26. https://doi.org/10.1145/3415164

Bode L, Vraga EK (2015) In related news, that was wrong: the correction of misinformation through related stories functionality in social media. J Commun 65(4):619–638. https://doi.org/10.1111/jcom.12166

Bondielli A, Marcelloni F (2019) A survey on fake news and rumour detection techniques. Inf Sci 497:38–55. https://doi.org/10.1016/j.ins.2019.05.035

Bovet A, Makse HA (2019) Influence of fake news in Twitter during the 2016 US presidential election. Nat Commun 10(1):1–14. https://doi.org/10.1038/s41467-018-07761-2

Brashier NM, Pennycook G, Berinsky AJ, Rand DG (2021) Timing matters when correcting fake news. Proc Natl Acad Sci. https://doi.org/10.1073/pnas.2020043118

Brewer PR, Young DG, Morreale M (2013) The impact of real news about “fake news’’: intertextual processes and political satire. Int J Public Opin Res 25(3):323–343. https://doi.org/10.1093/ijpor/edt015

Bringula RP, Catacutan-Bangit AE, Garcia MB, Gonzales JPS, Valderama AMC (2022) “Who is gullible to political disinformation?’’ Predicting susceptibility of university students to fake news. J Inf Technol Polit 19(2):165–179. https://doi.org/10.1080/19331681.2021.1945988

Buccafurri F, Lax G, Nicolazzo S, Nocera A (2017) Tweetchain: an alternative to blockchain for crowd-based applications. In: International conference on web engineering, Springer, Berlin, pp 386–393. https://doi.org/10.1007/978-3-319-60131-1_24

Burshtein S (2017) The true story on fake news. Intell Prop J 29(3):397–446

Cardaioli M, Cecconello S, Conti M, Pajola L, Turrin F (2020) Fake news spreaders profiling through behavioural analysis. In: CLEF (working notes)

Cardoso Durier da Silva F, Vieira R, Garcia AC (2019) Can machines learn to detect fake news? A survey focused on social media. In: Proceedings of the 52nd Hawaii international conference on system sciences. https://doi.org/10.24251/HICSS.2019.332

Carmi E, Yates SJ, Lockley E, Pawluczuk A (2020) Data citizenship: rethinking data literacy in the age of disinformation, misinformation, and malinformation. Intern Policy Rev 9(2):1–22 https://doi.org/10.14763/2020.2.1481

Celliers M, Hattingh M (2020) A systematic review on fake news themes reported in literature. In: Conference on e-Business, e-Services and e-Society. Springer, Berlin, pp 223–234. https://doi.org/10.1007/978-3-030-45002-1_19

Chen Y, Li Q, Wang H (2018) Towards trusted social networks with blockchain technology. arXiv preprint arXiv:1801.02796

Cheng L, Guo R, Shu K, Liu H (2020) Towards causal understanding of fake news dissemination. arXiv preprint arXiv:2010.10580

Chiu MM, Oh YW (2021) How fake news differs from personal lies. Am Behav Sci 65(2):243–258. https://doi.org/10.1177/0002764220910243

Chung M, Kim N (2021) When I learn the news is false: how fact-checking information stems the spread of fake news via third-person perception. Hum Commun Res 47(1):1–24. https://doi.org/10.1093/hcr/hqaa010

Clarke J, Chen H, Du D, Hu YJ (2020) Fake news, investor attention, and market reaction. Inf Syst Res. https://doi.org/10.1287/isre.2019.0910

Clayton K, Blair S, Busam JA, Forstner S, Glance J, Green G, Kawata A, Kovvuri A, Martin J, Morgan E et al (2020) Real solutions for fake news? Measuring the effectiveness of general warnings and fact-check tags in reducing belief in false stories on social media. Polit Behav 42(4):1073–1095. https://doi.org/10.1007/s11109-019-09533-0

Collins B, Hoang DT, Nguyen NT, Hwang D (2020) Fake news types and detection models on social media a state-of-the-art survey. In: Asian conference on intelligent information and database systems. Springer, Berlin, pp 562–573 https://doi.org/10.1007/978-981-15-3380-8_49

Conroy NK, Rubin VL, Chen Y (2015) Automatic deception detection: methods for finding fake news. Proc Assoc Inf Sci Technol 52(1):1–4. https://doi.org/10.1002/pra2.2015.145052010082

Cooke NA (2017) Posttruth, truthiness, and alternative facts: Information behavior and critical information consumption for a new age. Libr Q 87(3):211–221. https://doi.org/10.1086/692298

Article   MathSciNet   Google Scholar  

Coscia M, Rossi L (2020) Distortions of political bias in crowdsourced misinformation flagging. J R Soc Interface 17(167):20200020. https://doi.org/10.1098/rsif.2020.0020

Dame Adjin-Tettey T (2022) Combating fake news, disinformation, and misinformation: experimental evidence for media literacy education. Cogent Arts Human 9(1):2037229. https://doi.org/10.1080/23311983.2022.2037229

Deepak S, Chitturi B (2020) Deep neural approach to fake-news identification. Procedia Comput Sci 167:2236–2243. https://doi.org/10.1016/j.procs.2020.03.276

de Cock Buning M (2018) A multi-dimensional approach to disinformation: report of the independent high level group on fake news and online disinformation. Publications Office of the European Union

Del Vicario M, Quattrociocchi W, Scala A, Zollo F (2019) Polarization and fake news: early warning of potential misinformation targets. ACM Trans Web (TWEB) 13(2):1–22. https://doi.org/10.1145/3316809

Demuyakor J, Opata EM (2022) Fake news on social media: predicting which media format influences fake news most on facebook. J Intell Commun. https://doi.org/10.54963/jic.v2i1.56

Derakhshan H, Wardle C (2017) Information disorder: definitions. In: Understanding and addressing the disinformation ecosystem, pp 5–12

Desai AN, Ruidera D, Steinbrink JM, Granwehr B, Lee DH (2022) Misinformation and disinformation: the potential disadvantages of social media in infectious disease and how to combat them. Clin Infect Dis 74(Supplement–3):e34–e39. https://doi.org/10.1093/cid/ciac109

Di Domenico G, Sit J, Ishizaka A, Nunan D (2021) Fake news, social media and marketing: a systematic review. J Bus Res 124:329–341. https://doi.org/10.1016/j.jbusres.2020.11.037

Dias N, Pennycook G, Rand DG (2020) Emphasizing publishers does not effectively reduce susceptibility to misinformation on social media. Harv Kennedy School Misinform Rev. https://doi.org/10.37016/mr-2020-001

DiCicco KW, Agarwal N (2020) Blockchain technology-based solutions to fight misinformation: a survey. In: Disinformation, misinformation, and fake news in social media. Springer, Berlin, pp 267–281, https://doi.org/10.1007/978-3-030-42699-6_14

Douglas KM, Uscinski JE, Sutton RM, Cichocka A, Nefes T, Ang CS, Deravi F (2019) Understanding conspiracy theories. Polit Psychol 40:3–35. https://doi.org/10.1111/pops.12568

Edgerly S, Mourão RR, Thorson E, Tham SM (2020) When do audiences verify? How perceptions about message and source influence audience verification of news headlines. J Mass Commun Q 97(1):52–71. https://doi.org/10.1177/1077699019864680

Egelhofer JL, Lecheler S (2019) Fake news as a two-dimensional phenomenon: a framework and research agenda. Ann Int Commun Assoc 43(2):97–116. https://doi.org/10.1080/23808985.2019.1602782

Elhadad MK, Li KF, Gebali F (2019) A novel approach for selecting hybrid features from online news textual metadata for fake news detection. In: International conference on p2p, parallel, grid, cloud and internet computing. Springer, Berlin, pp 914–925, https://doi.org/10.1007/978-3-030-33509-0_86

ERGA (2018) Fake news, and the information disorder. European Broadcasting Union (EBU)

ERGA (2021) Notions of disinformation and related concepts. European Regulators Group for Audiovisual Media Services (ERGA)

Escolà-Gascón Á (2021) New techniques to measure lie detection using Covid-19 fake news and the Multivariable Multiaxial Suggestibility Inventory-2 (MMSI-2). Comput Hum Behav Rep 3:100049. https://doi.org/10.1016/j.chbr.2020.100049

Fazio L (2020) Pausing to consider why a headline is true or false can help reduce the sharing of false news. Harv Kennedy School Misinformation Rev. https://doi.org/10.37016/mr-2020-009

Ferrara E, Varol O, Davis C, Menczer F, Flammini A (2016) The rise of social bots. Commun ACM 59(7):96–104. https://doi.org/10.1145/2818717

Flynn D, Nyhan B, Reifler J (2017) The nature and origins of misperceptions: understanding false and unsupported beliefs about politics. Polit Psychol 38:127–150. https://doi.org/10.1111/pops.12394

Fraga-Lamas P, Fernández-Caramés TM (2020) Fake news, disinformation, and deepfakes: leveraging distributed ledger technologies and blockchain to combat digital deception and counterfeit reality. IT Prof 22(2):53–59. https://doi.org/10.1109/MITP.2020.2977589

Freeman D, Waite F, Rosebrock L, Petit A, Causier C, East A, Jenner L, Teale AL, Carr L, Mulhall S et al (2020) Coronavirus conspiracy beliefs, mistrust, and compliance with government guidelines in England. Psychol Med. https://doi.org/10.1017/S0033291720001890

Friggeri A, Adamic L, Eckles D, Cheng J (2014) Rumor cascades. In: Proceedings of the international AAAI conference on web and social media

García SA, García GG, Prieto MS, Moreno Guerrero AJ, Rodríguez Jiménez C (2020) The impact of term fake news on the scientific community. Scientific performance and mapping in web of science. Soc Sci. https://doi.org/10.3390/socsci9050073

Garrett RK, Bond RM (2021) Conservatives’ susceptibility to political misperceptions. Sci Adv. https://doi.org/10.1126/sciadv.abf1234

Giachanou A, Ríssola EA, Ghanem B, Crestani F, Rosso P (2020) The role of personality and linguistic patterns in discriminating between fake news spreaders and fact checkers. In: International conference on applications of natural language to information systems. Springer, Berlin, pp 181–192 https://doi.org/10.1007/978-3-030-51310-8_17

Golbeck J, Mauriello M, Auxier B, Bhanushali KH, Bonk C, Bouzaghrane MA, Buntain C, Chanduka R, Cheakalos P, Everett JB et al (2018) Fake news vs satire: a dataset and analysis. In: Proceedings of the 10th ACM conference on web science, pp 17–21, https://doi.org/10.1145/3201064.3201100

Goldani MH, Momtazi S, Safabakhsh R (2021) Detecting fake news with capsule neural networks. Appl Soft Comput 101:106991. https://doi.org/10.1016/j.asoc.2020.106991

Goldstein I, Yang L (2019) Good disclosure, bad disclosure. J Financ Econ 131(1):118–138. https://doi.org/10.1016/j.jfineco.2018.08.004

Grinberg N, Joseph K, Friedland L, Swire-Thompson B, Lazer D (2019) Fake news on Twitter during the 2016 US presidential election. Science 363(6425):374–378. https://doi.org/10.1126/science.aau2706

Guadagno RE, Guttieri K (2021) Fake news and information warfare: an examination of the political and psychological processes from the digital sphere to the real world. In: Research anthology on fake news, political warfare, and combatting the spread of misinformation. IGI Global, pp 218–242 https://doi.org/10.4018/978-1-7998-7291-7.ch013

Guess A, Nagler J, Tucker J (2019) Less than you think: prevalence and predictors of fake news dissemination on Facebook. Sci Adv. https://doi.org/10.1126/sciadv.aau4586

Guo C, Cao J, Zhang X, Shu K, Yu M (2019) Exploiting emotions for fake news detection on social media. arXiv preprint arXiv:1903.01728

Guo B, Ding Y, Yao L, Liang Y, Yu Z (2020) The future of false information detection on social media: new perspectives and trends. ACM Comput Surv (CSUR) 53(4):1–36. https://doi.org/10.1145/3393880

Gupta A, Li H, Farnoush A, Jiang W (2022) Understanding patterns of covid infodemic: a systematic and pragmatic approach to curb fake news. J Bus Res 140:670–683. https://doi.org/10.1016/j.jbusres.2021.11.032

Ha L, Andreu Perez L, Ray R (2021) Mapping recent development in scholarship on fake news and misinformation, 2008 to 2017: disciplinary contribution, topics, and impact. Am Behav Sci 65(2):290–315. https://doi.org/10.1177/0002764219869402

Habib A, Asghar MZ, Khan A, Habib A, Khan A (2019) False information detection in online content and its role in decision making: a systematic literature review. Soc Netw Anal Min 9(1):1–20. https://doi.org/10.1007/s13278-019-0595-5

Hage H, Aïmeur E, Guedidi A (2021) Understanding the landscape of online deception. In: Research anthology on fake news, political warfare, and combatting the spread of misinformation. IGI Global, pp 39–66. https://doi.org/10.4018/978-1-7998-2543-2.ch014

Hakak S, Alazab M, Khan S, Gadekallu TR, Maddikunta PKR, Khan WZ (2021) An ensemble machine learning approach through effective feature extraction to classify fake news. Futur Gener Comput Syst 117:47–58. https://doi.org/10.1016/j.future.2020.11.022

Hamdi T, Slimi H, Bounhas I, Slimani Y (2020) A hybrid approach for fake news detection in Twitter based on user features and graph embedding. In: International conference on distributed computing and internet technology. Springer, Berlin, pp 266–280. https://doi.org/10.1007/978-3-030-36987-3_17

Hameleers M (2022) Separating truth from lies: comparing the effects of news media literacy interventions and fact-checkers in response to political misinformation in the us and netherlands. Inf Commun Soc 25(1):110–126. https://doi.org/10.1080/1369118X.2020.1764603

Hameleers M, Powell TE, Van Der Meer TG, Bos L (2020) A picture paints a thousand lies? The effects and mechanisms of multimodal disinformation and rebuttals disseminated via social media. Polit Commun 37(2):281–301. https://doi.org/10.1080/10584609.2019.1674979

Hameleers M, Brosius A, de Vreese CH (2022) Whom to trust? media exposure patterns of citizens with perceptions of misinformation and disinformation related to the news media. Eur J Commun. https://doi.org/10.1177/02673231211072667

Hartley K, Vu MK (2020) Fighting fake news in the Covid-19 era: policy insights from an equilibrium model. Policy Sci 53(4):735–758. https://doi.org/10.1007/s11077-020-09405-z

Hasan HR, Salah K (2019) Combating deepfake videos using blockchain and smart contracts. IEEE Access 7:41596–41606. https://doi.org/10.1109/ACCESS.2019.2905689

Hiriyannaiah S, Srinivas A, Shetty GK, Siddesh G, Srinivasa K (2020) A computationally intelligent agent for detecting fake news using generative adversarial networks. Hybrid computational intelligence: challenges and applications. pp 69–96 https://doi.org/10.1016/B978-0-12-818699-2.00004-4

Hosseinimotlagh S, Papalexakis EE (2018) Unsupervised content-based identification of fake news articles with tensor decomposition ensembles. In: Proceedings of the workshop on misinformation and misbehavior mining on the web (MIS2)

Huckle S, White M (2017) Fake news: a technological approach to proving the origins of content, using blockchains. Big Data 5(4):356–371. https://doi.org/10.1089/big.2017.0071

Huffaker JS, Kummerfeld JK, Lasecki WS, Ackerman MS (2020) Crowdsourced detection of emotionally manipulative language. In: Proceedings of the 2020 CHI conference on human factors in computing systems. pp 1–14 https://doi.org/10.1145/3313831.3376375

Ireton C, Posetti J (2018) Journalism, fake news & disinformation: handbook for journalism education and training. UNESCO Publishing, Paris

Islam MR, Liu S, Wang X, Xu G (2020) Deep learning for misinformation detection on online social networks: a survey and new perspectives. Soc Netw Anal Min 10(1):1–20. https://doi.org/10.1007/s13278-020-00696-x

Ismailov M, Tsikerdekis M, Zeadally S (2020) Vulnerabilities to online social network identity deception detection research and recommendations for mitigation. Fut Internet 12(9):148. https://doi.org/10.3390/fi12090148

Jakesch M, Koren M, Evtushenko A, Naaman M (2019) The role of source and expressive responding in political news evaluation. In: Computation and journalism symposium

Jamieson KH (2020) Cyberwar: how Russian hackers and trolls helped elect a president: what we don’t, can’t, and do know. Oxford University Press, Oxford. https://doi.org/10.1093/poq/nfy049

Book   Google Scholar  

Jiang S, Chen X, Zhang L, Chen S, Liu H (2019) User-characteristic enhanced model for fake news detection in social media. In: CCF International conference on natural language processing and Chinese computing, Springer, Berlin, pp 634–646. https://doi.org/10.1007/978-3-030-32233-5_49

Jin Z, Cao J, Zhang Y, Luo J (2016) News verification by exploiting conflicting social viewpoints in microblogs. In: Proceedings of the AAAI conference on artificial intelligence

Jing TW, Murugesan RK (2018) A theoretical framework to build trust and prevent fake news in social media using blockchain. In: International conference of reliable information and communication technology. Springer, Berlin, pp 955–962, https://doi.org/10.1007/978-3-319-99007-1_88

Jones-Jang SM, Mortensen T, Liu J (2021) Does media literacy help identification of fake news? Information literacy helps, but other literacies don’t. Am Behav Sci 65(2):371–388. https://doi.org/10.1177/0002764219869406

Jungherr A, Schroeder R (2021) Disinformation and the structural transformations of the public arena: addressing the actual challenges to democracy. Soc Media Soc. https://doi.org/10.1177/2056305121988928

Kaliyar RK (2018) Fake news detection using a deep neural network. In: 2018 4th international conference on computing communication and automation (ICCCA), IEEE, pp 1–7 https://doi.org/10.1109/CCAA.2018.8777343

Kaliyar RK, Goswami A, Narang P, Sinha S (2020) Fndnet—a deep convolutional neural network for fake news detection. Cogn Syst Res 61:32–44. https://doi.org/10.1016/j.cogsys.2019.12.005

Kapantai E, Christopoulou A, Berberidis C, Peristeras V (2021) A systematic literature review on disinformation: toward a unified taxonomical framework. New Media Soc 23(5):1301–1326. https://doi.org/10.1177/1461444820959296

Kapusta J, Benko L, Munk M (2019) Fake news identification based on sentiment and frequency analysis. In: International conference Europe middle east and North Africa information systems and technologies to support learning. Springer, Berlin, pp 400–409, 10.1007/978-3-030-36778-7_44

Kaur S, Kumar P, Kumaraguru P (2020) Automating fake news detection system using multi-level voting model. Soft Comput 24(12):9049–9069. https://doi.org/10.1007/s00500-019-04436-y

Khan SA, Alkawaz MH, Zangana HM (2019) The use and abuse of social media for spreading fake news. In: 2019 IEEE international conference on automatic control and intelligent systems (I2CACIS), IEEE, pp 145–148. https://doi.org/10.1109/I2CACIS.2019.8825029

Kim J, Tabibian B, Oh A, Schölkopf B, Gomez-Rodriguez M (2018) Leveraging the crowd to detect and reduce the spread of fake news and misinformation. In: Proceedings of the eleventh ACM international conference on web search and data mining, pp 324–332. https://doi.org/10.1145/3159652.3159734

Klein D, Wueller J (2017) Fake news: a legal perspective. J Internet Law 20(10):5–13

Kogan S, Moskowitz TJ, Niessner M (2019) Fake news: evidence from financial markets. Available at SSRN 3237763

Kuklinski JH, Quirk PJ, Jerit J, Schwieder D, Rich RF (2000) Misinformation and the currency of democratic citizenship. J Polit 62(3):790–816. https://doi.org/10.1111/0022-3816.00033

Kumar S, Shah N (2018) False information on web and social media: a survey. arXiv preprint arXiv:1804.08559

Kumar S, West R, Leskovec J (2016) Disinformation on the web: impact, characteristics, and detection of Wikipedia hoaxes. In: Proceedings of the 25th international conference on world wide web, pp 591–602. https://doi.org/10.1145/2872427.2883085

La Barbera D, Roitero K, Demartini G, Mizzaro S, Spina D (2020) Crowdsourcing truthfulness: the impact of judgment scale and assessor bias. In: European conference on information retrieval. Springer, Berlin, pp 207–214. https://doi.org/10.1007/978-3-030-45442-5_26

Lanius C, Weber R, MacKenzie WI (2021) Use of bot and content flags to limit the spread of misinformation among social networks: a behavior and attitude survey. Soc Netw Anal Min 11(1):1–15. https://doi.org/10.1007/s13278-021-00739-x

Lazer DM, Baum MA, Benkler Y, Berinsky AJ, Greenhill KM, Menczer F, Metzger MJ, Nyhan B, Pennycook G, Rothschild D et al (2018) The science of fake news. Science 359(6380):1094–1096. https://doi.org/10.1126/science.aao2998

Le T, Shu K, Molina MD, Lee D, Sundar SS, Liu H (2019) 5 sources of clickbaits you should know! Using synthetic clickbaits to improve prediction and distinguish between bot-generated and human-written headlines. In: 2019 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM). IEEE, pp 33–40. https://doi.org/10.1145/3341161.3342875

Lewandowsky S (2020) Climate change, disinformation, and how to combat it. In: Annual Review of Public Health 42. https://doi.org/10.1146/annurev-publhealth-090419-102409

Liu Y, Wu YF (2018) Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI conference on artificial intelligence, pp 354–361

Luo M, Hancock JT, Markowitz DM (2022) Credibility perceptions and detection accuracy of fake news headlines on social media: effects of truth-bias and endorsement cues. Commun Res 49(2):171–195. https://doi.org/10.1177/0093650220921321

Lutzke L, Drummond C, Slovic P, Árvai J (2019) Priming critical thinking: simple interventions limit the influence of fake news about climate change on Facebook. Glob Environ Chang 58:101964. https://doi.org/10.1016/j.gloenvcha.2019.101964

Maertens R, Anseel F, van der Linden S (2020) Combatting climate change misinformation: evidence for longevity of inoculation and consensus messaging effects. J Environ Psychol 70:101455. https://doi.org/10.1016/j.jenvp.2020.101455

Mahabub A (2020) A robust technique of fake news detection using ensemble voting classifier and comparison with other classifiers. SN Applied Sciences 2(4):1–9. https://doi.org/10.1007/s42452-020-2326-y

Mahbub S, Pardede E, Kayes A, Rahayu W (2019) Controlling astroturfing on the internet: a survey on detection techniques and research challenges. Int J Web Grid Serv 15(2):139–158. https://doi.org/10.1504/IJWGS.2019.099561

Marsden C, Meyer T, Brown I (2020) Platform values and democratic elections: how can the law regulate digital disinformation? Comput Law Secur Rev 36:105373. https://doi.org/10.1016/j.clsr.2019.105373

Masciari E, Moscato V, Picariello A, Sperlí G (2020) Detecting fake news by image analysis. In: Proceedings of the 24th symposium on international database engineering and applications, pp 1–5. https://doi.org/10.1145/3410566.3410599

Mazzeo V, Rapisarda A (2022) Investigating fake and reliable news sources using complex networks analysis. Front Phys 10:886544. https://doi.org/10.3389/fphy.2022.886544

McGrew S (2020) Learning to evaluate: an intervention in civic online reasoning. Comput Educ 145:103711. https://doi.org/10.1016/j.compedu.2019.103711

McGrew S, Breakstone J, Ortega T, Smith M, Wineburg S (2018) Can students evaluate online sources? Learning from assessments of civic online reasoning. Theory Res Soc Educ 46(2):165–193. https://doi.org/10.1080/00933104.2017.1416320

Meel P, Vishwakarma DK (2020) Fake news, rumor, information pollution in social media and web: a contemporary survey of state-of-the-arts, challenges and opportunities. Expert Syst Appl 153:112986. https://doi.org/10.1016/j.eswa.2019.112986

Meese J, Frith J, Wilken R (2020) Covid-19, 5G conspiracies and infrastructural futures. Media Int Aust 177(1):30–46. https://doi.org/10.1177/1329878X20952165

Metzger MJ, Hartsell EH, Flanagin AJ (2020) Cognitive dissonance or credibility? A comparison of two theoretical explanations for selective exposure to partisan news. Commun Res 47(1):3–28. https://doi.org/10.1177/0093650215613136

Micallef N, He B, Kumar S, Ahamad M, Memon N (2020) The role of the crowd in countering misinformation: a case study of the Covid-19 infodemic. arXiv preprint arXiv:2011.05773

Mihailidis P, Viotty S (2017) Spreadable spectacle in digital culture: civic expression, fake news, and the role of media literacies in “post-fact society. Am Behav Sci 61(4):441–454. https://doi.org/10.1177/0002764217701217

Mishra R (2020) Fake news detection using higher-order user to user mutual-attention progression in propagation paths. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pp 652–653

Mishra S, Shukla P, Agarwal R (2022) Analyzing machine learning enabled fake news detection techniques for diversified datasets. Wirel Commun Mobile Comput. https://doi.org/10.1155/2022/1575365

Molina MD, Sundar SS, Le T, Lee D (2021) “Fake news’’ is not simply false information: a concept explication and taxonomy of online content. Am Behav Sci 65(2):180–212. https://doi.org/10.1177/0002764219878224

Moro C, Birt JR (2022) Review bombing is a dirty practice, but research shows games do benefit from online feedback. Conversation. https://research.bond.edu.au/en/publications/review-bombing-is-a-dirty-practice-but-research-shows-games-do-be

Mustafaraj E, Metaxas PT (2017) The fake news spreading plague: was it preventable? In: Proceedings of the 2017 ACM on web science conference, pp 235–239. https://doi.org/10.1145/3091478.3091523

Nagel TW (2022) Measuring fake news acumen using a news media literacy instrument. J Media Liter Educ 14(1):29–42. https://doi.org/10.23860/JMLE-2022-14-1-3

Nakov P (2020) Can we spot the “fake news” before it was even written? arXiv preprint arXiv:2008.04374

Nekmat E (2020) Nudge effect of fact-check alerts: source influence and media skepticism on sharing of news misinformation in social media. Soc Media Soc. https://doi.org/10.1177/2056305119897322

Nygren T, Brounéus F, Svensson G (2019) Diversity and credibility in young people’s news feeds: a foundation for teaching and learning citizenship in a digital era. J Soc Sci Educ 18(2):87–109. https://doi.org/10.4119/jsse-917

Nyhan B, Reifler J (2015) Displacing misinformation about events: an experimental test of causal corrections. J Exp Polit Sci 2(1):81–93. https://doi.org/10.1017/XPS.2014.22

Nyhan B, Porter E, Reifler J, Wood TJ (2020) Taking fact-checks literally but not seriously? The effects of journalistic fact-checking on factual beliefs and candidate favorability. Polit Behav 42(3):939–960. https://doi.org/10.1007/s11109-019-09528-x

Nyow NX, Chua HN (2019) Detecting fake news with tweets’ properties. In: 2019 IEEE conference on application, information and network security (AINS), IEEE, pp 24–29. https://doi.org/10.1109/AINS47559.2019.8968706

Ochoa IS, de Mello G, Silva LA, Gomes AJ, Fernandes AM, Leithardt VRQ (2019) Fakechain: a blockchain architecture to ensure trust in social media networks. In: International conference on the quality of information and communications technology. Springer, Berlin, pp 105–118. https://doi.org/10.1007/978-3-030-29238-6_8

Ozbay FA, Alatas B (2020) Fake news detection within online social media using supervised artificial intelligence algorithms. Physica A 540:123174. https://doi.org/10.1016/j.physa.2019.123174

Ozturk P, Li H, Sakamoto Y (2015) Combating rumor spread on social media: the effectiveness of refutation and warning. In: 2015 48th Hawaii international conference on system sciences, IEEE, pp 2406–2414. https://doi.org/10.1109/HICSS.2015.288

Parikh SB, Atrey PK (2018) Media-rich fake news detection: a survey. In: 2018 IEEE conference on multimedia information processing and retrieval (MIPR), IEEE, pp 436–441. https://doi.org/10.1109/MIPR.2018.00093

Parrish K (2018) Deep learning & machine learning: what’s the difference? Online: https://parsers.me/deep-learning-machine-learning-whats-the-difference/ . Accessed 20 May 2020

Paschen J (2019) Investigating the emotional appeal of fake news using artificial intelligence and human contributions. J Prod Brand Manag 29(2):223–233. https://doi.org/10.1108/JPBM-12-2018-2179

Pathak A, Srihari RK (2019) Breaking! Presenting fake news corpus for automated fact checking. In: Proceedings of the 57th annual meeting of the association for computational linguistics: student research workshop, pp 357–362

Peng J, Detchon S, Choo KKR, Ashman H (2017) Astroturfing detection in social media: a binary n-gram-based approach. Concurr Comput: Pract Exp 29(17):e4013. https://doi.org/10.1002/cpe.4013

Pennycook G, Rand DG (2019) Fighting misinformation on social media using crowdsourced judgments of news source quality. Proc Natl Acad Sci 116(7):2521–2526. https://doi.org/10.1073/pnas.1806781116

Pennycook G, Rand DG (2020) Who falls for fake news? The roles of bullshit receptivity, overclaiming, familiarity, and analytic thinking. J Pers 88(2):185–200. https://doi.org/10.1111/jopy.12476

Pennycook G, Bear A, Collins ET, Rand DG (2020a) The implied truth effect: attaching warnings to a subset of fake news headlines increases perceived accuracy of headlines without warnings. Manag Sci 66(11):4944–4957. https://doi.org/10.1287/mnsc.2019.3478

Pennycook G, McPhetres J, Zhang Y, Lu JG, Rand DG (2020b) Fighting Covid-19 misinformation on social media: experimental evidence for a scalable accuracy-nudge intervention. Psychol Sci 31(7):770–780. https://doi.org/10.1177/0956797620939054

Potthast M, Kiesel J, Reinartz K, Bevendorff J, Stein B (2017) A stylometric inquiry into hyperpartisan and fake news. arXiv preprint arXiv:1702.05638

Previti M, Rodriguez-Fernandez V, Camacho D, Carchiolo V, Malgeri M (2020) Fake news detection using time series and user features classification. In: International conference on the applications of evolutionary computation (Part of EvoStar), Springer, Berlin, pp 339–353. https://doi.org/10.1007/978-3-030-43722-0_22

Przybyla P (2020) Capturing the style of fake news. In: Proceedings of the AAAI conference on artificial intelligence, pp 490–497. https://doi.org/10.1609/aaai.v34i01.5386

Qayyum A, Qadir J, Janjua MU, Sher F (2019) Using blockchain to rein in the new post-truth world and check the spread of fake news. IT Prof 21(4):16–24. https://doi.org/10.1109/MITP.2019.2910503

Qian F, Gong C, Sharma K, Liu Y (2018) Neural user response generator: fake news detection with collective user intelligence. In: IJCAI, vol 18, pp 3834–3840. https://doi.org/10.24963/ijcai.2018/533

Raza S, Ding C (2022) Fake news detection based on news content and social contexts: a transformer-based approach. Int J Data Sci Anal 13(4):335–362. https://doi.org/10.1007/s41060-021-00302-z

Ricard J, Medeiros J (2020) Using misinformation as a political weapon: Covid-19 and Bolsonaro in Brazil. Harv Kennedy School misinformation Rev 1(3). https://misinforeview.hks.harvard.edu/article/using-misinformation-as-a-political-weapon-covid-19-and-bolsonaro-in-brazil/

Roozenbeek J, van der Linden S (2019) Fake news game confers psychological resistance against online misinformation. Palgrave Commun 5(1):1–10. https://doi.org/10.1057/s41599-019-0279-9

Roozenbeek J, van der Linden S, Nygren T (2020a) Prebunking interventions based on the psychological theory of “inoculation’’ can reduce susceptibility to misinformation across cultures. Harv Kennedy School Misinformation Rev. https://doi.org/10.37016//mr-2020-008

Roozenbeek J, Schneider CR, Dryhurst S, Kerr J, Freeman AL, Recchia G, Van Der Bles AM, Van Der Linden S (2020b) Susceptibility to misinformation about Covid-19 around the world. R Soc Open Sci 7(10):201199. https://doi.org/10.1098/rsos.201199

Rubin VL, Conroy N, Chen Y, Cornwell S (2016) Fake news or truth? Using satirical cues to detect potentially misleading news. In: Proceedings of the second workshop on computational approaches to deception detection, pp 7–17

Ruchansky N, Seo S, Liu Y (2017) Csi: a hybrid deep model for fake news detection. In: Proceedings of the 2017 ACM on conference on information and knowledge management, pp 797–806. https://doi.org/10.1145/3132847.3132877

Schuyler AJ (2019) Regulating facts: a procedural framework for identifying, excluding, and deterring the intentional or knowing proliferation of fake news online. Univ Ill JL Technol Pol’y, vol 2019, pp 211–240

Shae Z, Tsai J (2019) AI blockchain platform for trusting news. In: 2019 IEEE 39th international conference on distributed computing systems (ICDCS), IEEE, pp 1610–1619. https://doi.org/10.1109/ICDCS.2019.00160

Shang W, Liu M, Lin W, Jia M (2018) Tracing the source of news based on blockchain. In: 2018 IEEE/ACIS 17th international conference on computer and information science (ICIS), IEEE, pp 377–381. https://doi.org/10.1109/ICIS.2018.8466516

Shao C, Ciampaglia GL, Flammini A, Menczer F (2016) Hoaxy: A platform for tracking online misinformation. In: Proceedings of the 25th international conference companion on world wide web, pp 745–750. https://doi.org/10.1145/2872518.2890098

Shao C, Ciampaglia GL, Varol O, Yang KC, Flammini A, Menczer F (2018) The spread of low-credibility content by social bots. Nat Commun 9(1):1–9. https://doi.org/10.1038/s41467-018-06930-7

Shao C, Hui PM, Wang L, Jiang X, Flammini A, Menczer F, Ciampaglia GL (2018) Anatomy of an online misinformation network. PLoS ONE 13(4):e0196087. https://doi.org/10.1371/journal.pone.0196087

Sharma K, Qian F, Jiang H, Ruchansky N, Zhang M, Liu Y (2019) Combating fake news: a survey on identification and mitigation techniques. ACM Trans Intell Syst Technol (TIST) 10(3):1–42. https://doi.org/10.1145/3305260

Sharma K, Seo S, Meng C, Rambhatla S, Liu Y (2020) Covid-19 on social media: analyzing misinformation in Twitter conversations. arXiv preprint arXiv:2003.12309

Shen C, Kasra M, Pan W, Bassett GA, Malloch Y, O’Brien JF (2019) Fake images: the effects of source, intermediary, and digital media literacy on contextual assessment of image credibility online. New Media Soc 21(2):438–463. https://doi.org/10.1177/1461444818799526

Sherman IN, Redmiles EM, Stokes JW (2020) Designing indicators to combat fake media. arXiv preprint arXiv:2010.00544

Shi P, Zhang Z, Choo KKR (2019) Detecting malicious social bots based on clickstream sequences. IEEE Access 7:28855–28862. https://doi.org/10.1109/ACCESS.2019.2901864

Shu K, Sliva A, Wang S, Tang J, Liu H (2017) Fake news detection on social media: a data mining perspective. ACM SIGKDD Explor Newsl 19(1):22–36. https://doi.org/10.1145/3137597.3137600

Shu K, Mahudeswaran D, Wang S, Lee D, Liu H (2018a) Fakenewsnet: a data repository with news content, social context and spatialtemporal information for studying fake news on social media. arXiv preprint arXiv:1809.01286 , https://doi.org/10.1089/big.2020.0062

Shu K, Wang S, Liu H (2018b) Understanding user profiles on social media for fake news detection. In: 2018 IEEE conference on multimedia information processing and retrieval (MIPR), IEEE, pp 430–435. https://doi.org/10.1109/MIPR.2018.00092

Shu K, Wang S, Liu H (2019a) Beyond news contents: the role of social context for fake news detection. In: Proceedings of the twelfth ACM international conference on web search and data mining, pp 312–320. https://doi.org/10.1145/3289600.3290994

Shu K, Zhou X, Wang S, Zafarani R, Liu H (2019b) The role of user profiles for fake news detection. In: Proceedings of the 2019 IEEE/ACM international conference on advances in social networks analysis and mining, pp 436–439. https://doi.org/10.1145/3341161.3342927

Shu K, Bhattacharjee A, Alatawi F, Nazer TH, Ding K, Karami M, Liu H (2020a) Combating disinformation in a social media age. Wiley Interdiscip Rev: Data Min Knowl Discov 10(6):e1385. https://doi.org/10.1002/widm.1385

Shu K, Mahudeswaran D, Wang S, Liu H (2020b) Hierarchical propagation networks for fake news detection: investigation and exploitation. Proc Int AAAI Conf Web Soc Media AAAI Press 14:626–637

Shu K, Wang S, Lee D, Liu H (2020c) Mining disinformation and fake news: concepts, methods, and recent advancements. In: Disinformation, misinformation, and fake news in social media. Springer, Berlin, pp 1–19 https://doi.org/10.1007/978-3-030-42699-6_1

Shu K, Zheng G, Li Y, Mukherjee S, Awadallah AH, Ruston S, Liu H (2020d) Early detection of fake news with multi-source weak social supervision. In: ECML/PKDD (3), pp 650–666

Singh VK, Ghosh I, Sonagara D (2021) Detecting fake news stories via multimodal analysis. J Am Soc Inf Sci 72(1):3–17. https://doi.org/10.1002/asi.24359

Sintos S, Agarwal PK, Yang J (2019) Selecting data to clean for fact checking: minimizing uncertainty vs. maximizing surprise. Proc VLDB Endowm 12(13), 2408–2421. https://doi.org/10.14778/3358701.3358708

Snow J (2017) Can AI win the war against fake news? MIT Technology Review Online: https://www.technologyreview.com/s/609717/can-ai-win-the-war-against-fake-news/ . Accessed 3 Oct. 2020

Song G, Kim S, Hwang H, Lee K (2019) Blockchain-based notarization for social media. In: 2019 IEEE international conference on consumer clectronics (ICCE), IEEE, pp 1–2 https://doi.org/10.1109/ICCE.2019.8661978

Starbird K, Arif A, Wilson T (2019) Disinformation as collaborative work: Surfacing the participatory nature of strategic information operations. In: Proceedings of the ACM on human–computer interaction, vol 3(CSCW), pp 1–26 https://doi.org/10.1145/3359229

Sterret D, Malato D, Benz J, Kantor L, Tompson T, Rosenstiel T, Sonderman J, Loker K, Swanson E (2018) Who shared it? How Americans decide what news to trust on social media. Technical report, Norc Working Paper Series, WP-2018-001, pp 1–24

Sutton RM, Douglas KM (2020) Conspiracy theories and the conspiracy mindset: implications for political ideology. Curr Opin Behav Sci 34:118–122. https://doi.org/10.1016/j.cobeha.2020.02.015

Tandoc EC Jr, Thomas RJ, Bishop L (2021) What is (fake) news? Analyzing news values (and more) in fake stories. Media Commun 9(1):110–119. https://doi.org/10.17645/mac.v9i1.3331

Tchakounté F, Faissal A, Atemkeng M, Ntyam A (2020) A reliable weighting scheme for the aggregation of crowd intelligence to detect fake news. Information 11(6):319. https://doi.org/10.3390/info11060319

Tchechmedjiev A, Fafalios P, Boland K, Gasquet M, Zloch M, Zapilko B, Dietze S, Todorov K (2019) Claimskg: a knowledge graph of fact-checked claims. In: International semantic web conference. Springer, Berlin, pp 309–324 https://doi.org/10.1007/978-3-030-30796-7_20

Treen KMd, Williams HT, O’Neill SJ (2020) Online misinformation about climate change. Wiley Interdiscip Rev Clim Change 11(5):e665. https://doi.org/10.1002/wcc.665

Tsang SJ (2020) Motivated fake news perception: the impact of news sources and policy support on audiences’ assessment of news fakeness. J Mass Commun Q. https://doi.org/10.1177/1077699020952129

Tschiatschek S, Singla A, Gomez Rodriguez M, Merchant A, Krause A (2018) Fake news detection in social networks via crowd signals. In: Companion proceedings of the the web conference 2018, pp 517–524. https://doi.org/10.1145/3184558.3188722

Uppada SK, Manasa K, Vidhathri B, Harini R, Sivaselvan B (2022) Novel approaches to fake news and fake account detection in OSNS: user social engagement and visual content centric model. Soc Netw Anal Min 12(1):1–19. https://doi.org/10.1007/s13278-022-00878-9

Van der Linden S, Roozenbeek J (2020) Psychological inoculation against fake news. In: Accepting, sharing, and correcting misinformation, the psychology of fake news. https://doi.org/10.4324/9780429295379-11

Van der Linden S, Panagopoulos C, Roozenbeek J (2020) You are fake news: political bias in perceptions of fake news. Media Cult Soc 42(3):460–470. https://doi.org/10.1177/0163443720906992

Valenzuela S, Muñiz C, Santos M (2022) Social media and belief in misinformation in mexico: a case of maximal panic, minimal effects? Int J Press Polit. https://doi.org/10.1177/19401612221088988

Vasu N, Ang B, Teo TA, Jayakumar S, Raizal M, Ahuja J (2018) Fake news: national security in the post-truth era. RSIS

Vereshchaka A, Cosimini S, Dong W (2020) Analyzing and distinguishing fake and real news to mitigate the problem of disinformation. In: Computational and mathematical organization theory, pp 1–15. https://doi.org/10.1007/s10588-020-09307-8

Verstraete M, Bambauer DE, Bambauer JR (2017) Identifying and countering fake news. Arizona legal studies discussion paper 73(17-15). https://doi.org/10.2139/ssrn.3007971

Vilmer J, Escorcia A, Guillaume M, Herrera J (2018) Information manipulation: a challenge for our democracies. In: Report by the Policy Planning Staff (CAPS) of the ministry for europe and foreign affairs, and the institute for strategic research (RSEM) of the Ministry for the Armed Forces

Vishwakarma DK, Varshney D, Yadav A (2019) Detection and veracity analysis of fake news via scrapping and authenticating the web search. Cogn Syst Res 58:217–229. https://doi.org/10.1016/j.cogsys.2019.07.004

Vlachos A, Riedel S (2014) Fact checking: task definition and dataset construction. In: Proceedings of the ACL 2014 workshop on language technologies and computational social science, pp 18–22. https://doi.org/10.3115/v1/W14-2508

von der Weth C, Abdul A, Fan S, Kankanhalli M (2020) Helping users tackle algorithmic threats on social media: a multimedia research agenda. In: Proceedings of the 28th ACM international conference on multimedia, pp 4425–4434. https://doi.org/10.1145/3394171.3414692

Vosoughi S, Roy D, Aral S (2018) The spread of true and false news online. Science 359(6380):1146–1151. https://doi.org/10.1126/science.aap9559

Vraga EK, Bode L (2017) Using expert sources to correct health misinformation in social media. Sci Commun 39(5):621–645. https://doi.org/10.1177/1075547017731776

Waldman AE (2017) The marketplace of fake news. Univ Pa J Const Law 20:845

Wang WY (2017) “Liar, liar pants on fire”: a new benchmark dataset for fake news detection. arXiv preprint arXiv:1705.00648

Wang L, Wang Y, de Melo G, Weikum G (2019a) Understanding archetypes of fake news via fine-grained classification. Soc Netw Anal Min 9(1):1–17. https://doi.org/10.1007/s13278-019-0580-z

Wang Y, Han H, Ding Y, Wang X, Liao Q (2019b) Learning contextual features with multi-head self-attention for fake news detection. In: International conference on cognitive computing. Springer, Berlin, pp 132–142. https://doi.org/10.1007/978-3-030-23407-2_11

Wang Y, McKee M, Torbica A, Stuckler D (2019c) Systematic literature review on the spread of health-related misinformation on social media. Soc Sci Med 240:112552. https://doi.org/10.1016/j.socscimed.2019.112552

Wang Y, Yang W, Ma F, Xu J, Zhong B, Deng Q, Gao J (2020) Weak supervision for fake news detection via reinforcement learning. In: Proceedings of the AAAI conference on artificial intelligence, pp 516–523. https://doi.org/10.1609/aaai.v34i01.5389

Wardle C (2017) Fake news. It’s complicated. Online: https://medium.com/1st-draft/fake-news-its-complicated-d0f773766c79 . Accessed 3 Oct 2020

Wardle C (2018) The need for smarter definitions and practical, timely empirical research on information disorder. Digit J 6(8):951–963. https://doi.org/10.1080/21670811.2018.1502047

Wardle C, Derakhshan H (2017) Information disorder: toward an interdisciplinary framework for research and policy making. Council Eur Rep 27:1–107

Weiss AP, Alwan A, Garcia EP, Garcia J (2020) Surveying fake news: assessing university faculty’s fragmented definition of fake news and its impact on teaching critical thinking. Int J Educ Integr 16(1):1–30. https://doi.org/10.1007/s40979-019-0049-x

Wu L, Liu H (2018) Tracing fake-news footprints: characterizing social media messages by how they propagate. In: Proceedings of the eleventh ACM international conference on web search and data mining, pp 637–645. https://doi.org/10.1145/3159652.3159677

Wu L, Rao Y (2020) Adaptive interaction fusion networks for fake news detection. arXiv preprint arXiv:2004.10009

Wu L, Morstatter F, Carley KM, Liu H (2019) Misinformation in social media: definition, manipulation, and detection. ACM SIGKDD Explor Newsl 21(2):80–90. https://doi.org/10.1145/3373464.3373475

Wu Y, Ngai EW, Wu P, Wu C (2022) Fake news on the internet: a literature review, synthesis and directions for future research. Intern Res. https://doi.org/10.1108/INTR-05-2021-0294

Xu K, Wang F, Wang H, Yang B (2019) Detecting fake news over online social media via domain reputations and content understanding. Tsinghua Sci Technol 25(1):20–27. https://doi.org/10.26599/TST.2018.9010139

Yang F, Pentyala SK, Mohseni S, Du M, Yuan H, Linder R, Ragan ED, Ji S, Hu X (2019a) Xfake: explainable fake news detector with visualizations. In: The world wide web conference, pp 3600–3604. https://doi.org/10.1145/3308558.3314119

Yang X, Li Y, Lyu S (2019b) Exposing deep fakes using inconsistent head poses. In: ICASSP 2019-2019 IEEE international conference on acoustics, speech and signal processing (ICASSP), IEEE, pp 8261–8265. https://doi.org/10.1109/ICASSP.2019.8683164

Yaqub W, Kakhidze O, Brockman ML, Memon N, Patil S (2020) Effects of credibility indicators on social media news sharing intent. In: Proceedings of the 2020 CHI conference on human factors in computing systems, pp 1–14. https://doi.org/10.1145/3313831.3376213

Yavary A, Sajedi H, Abadeh MS (2020) Information verification in social networks based on user feedback and news agencies. Soc Netw Anal Min 10(1):1–8. https://doi.org/10.1007/s13278-019-0616-4

Yazdi KM, Yazdi AM, Khodayi S, Hou J, Zhou W, Saedy S (2020) Improving fake news detection using k-means and support vector machine approaches. Int J Electron Commun Eng 14(2):38–42. https://doi.org/10.5281/zenodo.3669287

Zannettou S, Sirivianos M, Blackburn J, Kourtellis N (2019) The web of false information: rumors, fake news, hoaxes, clickbait, and various other shenanigans. J Data Inf Qual (JDIQ) 11(3):1–37. https://doi.org/10.1145/3309699

Zellers R, Holtzman A, Rashkin H, Bisk Y, Farhadi A, Roesner F, Choi Y (2019) Defending against neural fake news. arXiv preprint arXiv:1905.12616

Zhang X, Ghorbani AA (2020) An overview of online fake news: characterization, detection, and discussion. Inf Process Manag 57(2):102025. https://doi.org/10.1016/j.ipm.2019.03.004

Zhang J, Dong B, Philip SY (2020) Fakedetector: effective fake news detection with deep diffusive neural network. In: 2020 IEEE 36th international conference on data engineering (ICDE), IEEE, pp 1826–1829. 10.1109/ICDE48307.2020.00180

Zhang Q, Lipani A, Liang S, Yilmaz E (2019a) Reply-aided detection of misinformation via Bayesian deep learning. In: The world wide web conference, pp 2333–2343. https://doi.org/10.1145/3308558.3313718

Zhang X, Karaman S, Chang SF (2019b) Detecting and simulating artifacts in GAN fake images. In: 2019 IEEE international workshop on information forensics and security (WIFS), IEEE, pp 1–6 https://doi.org/10.1109/WIFS47025.2019.9035107

Zhou X, Zafarani R (2020) A survey of fake news: fundamental theories, detection methods, and opportunities. ACM Comput Surv (CSUR) 53(5):1–40. https://doi.org/10.1145/3395046

Zubiaga A, Aker A, Bontcheva K, Liakata M, Procter R (2018) Detection and resolution of rumours in social media: a survey. ACM Comput Surv (CSUR) 51(2):1–36. https://doi.org/10.1145/3161603

Download references

This work is supported in part by Canada’s Natural Sciences and Engineering Research Council.

Author information

Authors and affiliations.

Department of Computer Science and Operations Research (DIRO), University of Montreal, Montreal, Canada

Esma Aïmeur, Sabrine Amri & Gilles Brassard

You can also search for this author in PubMed   Google Scholar

Contributions

The order of authors is alphabetic as is customary in the third author’s field. The lead author was Sabrine Amri, who collected and analyzed the data and wrote a first draft of the paper, all along under the supervision and tight guidance of Esma Aïmeur. Gilles Brassard reviewed, criticized and polished the work into its final form.

Corresponding author

Correspondence to Sabrine Amri .

Ethics declarations

Conflict of interest.

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix: A Comparison of AI-based fake news detection techniques

This Appendix consists only in the rather long Table  11 . It shows a comparison of the fake news detection solutions based on artificial intelligence that we have reviewed according to their main approaches, the methodology that was used, and the models, as explained in Sect.  6.2.2 .

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Aïmeur, E., Amri, S. & Brassard, G. Fake news, disinformation and misinformation in social media: a review. Soc. Netw. Anal. Min. 13 , 30 (2023). https://doi.org/10.1007/s13278-023-01028-5

Download citation

Received : 20 October 2022

Revised : 07 January 2023

Accepted : 12 January 2023

Published : 09 February 2023

DOI : https://doi.org/10.1007/s13278-023-01028-5

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Disinformation
• Misinformation
• Information disorder
• Online deception
• Online social networks
• Find a journal
• Publish with us
• Track your research