argumentative essay about fake news brainly

‘Fake news’ – why people believe it and what can be done to counter it

Director Institute of Cultural Capital, University of Liverpool

Disclosure statement

This piece was commissioned by the Campaign for Social Science.

University of Liverpool provides funding as a founding partner of The Conversation UK.

View all partners

Barack Obama believes “fake news” is a threat to democracy. The outgoing US president said he was worried about the way that “so much active misinformation” can be “packaged very well” and presented as fact on people’s social media feeds. He told a recent conference in Germany:

If we are not serious about facts and what’s true and what’s not, if we can’t discriminate between serious arguments and propaganda, then we have problems.

But how do we distinguish between facts, legitimate debate and propaganda? Since the Brexit vote and the Donald Trump victory a huge amount of journalists’ ink has been used up discussing the impact of social media and the spread of “ fake news ” on political discourse, the functioning of democracy and on journalism. Detailed social science research is yet to emerge, though a lot can be learnt from existing studies of online and offline behaviour.

Matter of trust

Let’s start with a broad definition of “fake news” as information distributed via a medium – often for the benefit of specific social actors – that then proves unverifiable or materially incorrect. As has been noted, “fake news” used to be called propaganda. And there is an extensive social science literature on propaganda , its history, function and links to the state – both democratic and dictatorial.

In fact, as the investigations in the US and Italy show, one of the major sources of fake news is Russia. Full Fact , a site in the UK, is dedicated to rooting out media stories that play fast and loose with the truth – and there is no shortage.

An argument could be made that as the “mainstream” media have become seen as less trustworthy (rightly or wrongly) in the eyes of their audiences, it makes it hard to distinguish between those who have supposedly got a vested interest in telling the truth and those that don’t necessarily share the same ethical foundation. How does mainstream journalism that is also clearly politically biased – on all sides – claim the moral high ground? This problem certainly predates digital technology.

Bubbles and echo chambers

This leaves us with the question of whether social media makes it worse? Almost as much ink has been used up talking about social media “bubbles” – how we all tend to talk with people who share our outlook – something, again, which is not necessarily unique to the digital age. This operates in two distinct ways.

Bubbles are a product of class and cultural position. A recent UK study on social class pointed this out. An important subtlety here is that though those with higher “social status” may congregate, they are also likely to have more socially diverse acquaintance networks than those in lower income and status groups. They are also likely to have a greater diversity of media, especially internet usage patterns . Not all bubbles are the same size nor as monochromatic and our social media bubbles reflect our everyday “offline” bubbles .

In fact social media bubbles may be very pertinent to journalist-politician interactions as one of the best-defined Twitter bubbles is the one that surrounds politicians and journalists.

This brings back into focus older models of media effects such as the two-step flow model where key “opinion leaders” – influential nodes in our social networks – have an impact on our consumption of media. Analyses of a “fake news story” appears to point – not to social media per se – but to how stories moving through social media can be picked up by leading sites and actors with many followers and become amplified.

The false assumption in a tweet from an individual becomes a “fake news” story on an ideologically-driven news site or becomes a tweet from the president-elect and becomes a “fact” for many. And we panic more about this as social media make both the message and how it moves very visible.

Outing fake news

What fuels this and can we address it? First, the economics of social media favour gossip, novelty, speed and “shareability”. They mistake sociability for social value. There is evidence that “fake news” that plays to existing prejudice is more likely to be “liked” and so generate more revenue for the creators. This is no different than “celebrity” magazines. Well researched and documented news is far less likely to be widely shared.

The other key point here is that – as Obama noted – it becomes hard to distinguish fake from fact, and there is evidence that many struggle to do this. As my colleagues and I argued nearly 20 years ago , digital media make it harder to distinguish the veracity of content simply by the physical format it comes in (broadsheet newspaper, high-quality news broadcast, textbook or tabloid story). Online news is harder to distinguish.

The next problem is that retracting “fake news” on social media is currently poorly supported by the technology. Though posts can be deleted, this is a passive act, less impactful than even the single-paragraph retractions in newspapers . In order to have an impact, it would be necessary not simply to delete posts but to highlight and require users to see and acknowledge items removed as “fake news”.

So whether or not fake news is a manifestation of the digital and social media age, it seems likely that social media is able to amplify the spread of misinformation. Their economics favour shareability over veracity and distribution over retraction. These are not technology “requirements” but choices – by the systems’ designers and their regulators (where there are any). And mainstream media may have tarnished their own reputation through “fake” and visibly ideological news coverage, opening the door to other news sources.

Understanding this complex mix of factors is the job of the social sciences. But maybe the real message here is that we as societies and individuals have questions to answer about educating people to read the news, about our choice not to regulate social media (as we do TV and print) and in our own behaviour – ask yourself, how often do you fact-check a story before reposting it?

• Future of media

GRAINS RESEARCH AND DEVELOPMENT CORPORATION CHAIRPERSON

Project Officer, Student Program Development

Faculty of Law - Academic Appointment Opportunities

Operations Manager

Audience Development Coordinator (fixed-term maternity cover)

How to combat fake news and disinformation

Subscribe to the center for technology innovation newsletter, darrell m. west darrell m. west senior fellow - center for technology innovation , douglas dillon chair in governmental studies.

December 18, 2017

Executive summary

Journalism is in a state of considerable flux. New digital platforms have unleashed innovative journalistic practices that enable novel forms of communication and greater global reach than at any point in human history. But on the other hand, disinformation and hoaxes that are popularly referred to as “fake news” are accelerating and affecting the way individuals interpret daily developments. Driven by foreign actors, citizen journalism, and the proliferation of talk radio and cable news, many information systems have become more polarized and contentious, and there has been a precipitous decline in public trust in traditional journalism.

Fake news and sophisticated disinformation campaigns are especially problematic in democratic systems, and there is growing debate on how to address these issues without undermining the benefits of digital media. In order to maintain an open, democratic system, it is important that government, business, and consumers work together to solve these problems. Governments should promote news literacy and strong professional journalism in their societies. The news industry must provide high-quality journalism in order to build public trust and correct fake news and disinformation without legitimizing them. Technology companies should invest in tools that identify fake news, reduce financial incentives for those who profit from disinformation, and improve online accountability. Educational institutions should make informing people about news literacy a high priority. Finally, individuals should follow a diversity of news sources, and be skeptical of what they read and watch.

The state of the news media

The news media landscape has changed dramatically over the past decades. Through digital sources, there has been a tremendous increase in the reach of journalism, social media, and public engagement. Checking for news online—whether through Google, Twitter, Facebook, major newspapers, or local media websites—has become ubiquitous, and smartphone alerts and mobile applications bring the latest developments to people instantaneously around the world. As of 2017, 93 percent of Americans say they receive news online. 1  When asked where they got online news in the last two hours, 36 percent named a news organization website or app; 35 percent said social media (which typically means a post from a news organization, but can be a friend’s commentary); 20 percent recalled a search engine; 15 percent indicated a news organization email, text, or alert; 9 percent said it was another source; and 7 percent named a family member email or text (see Figure 1). 2

In general, young people are most likely to get their news through online sources, relying heavily on mobile devices for their communications. According to the Pew Research Center, 55 percent of smartphone users receive news alerts on their devices. And about 47 percent of those receiving alerts click through to read the story. 3 Increasingly, people can customize information delivery to their personal preferences. For example, it is possible to sign up for news alerts from many organizations so that people hear news relevant to their particular interests.

There have been changes overtime in sources of news overall. Figure 2 shows the results for 2012 to 2017. It demonstrates that the biggest gain has been in reliance upon social media. In 2012-2013, 27 percent relied upon social media sites, compared to 51 percent who did so in 2017. 4 In contrast, the percentage of Americans relying upon print news has dropped from 38 to 22 percent.

A number of research organizations have found significant improvements in digital access around the world. For example, the Pew Research Center has documented through surveys in 21 emerging nations that internet usage has risen from 45 percent in 2013 to 54 percent in 2015. That number still trails the 87 percent usage figure seen in 11 developed countries, but there clearly have been major gains in many places around the world. 5

Social media sites are very popular in the developing world. As shown in Figure 3, 86 percent of Middle Eastern internet users rely upon social networks, compared to 82 percent in Latin America, 76 percent in Africa, 71 percent in the United States, 66 percent in Asia and the Pacific, and 65 percent in Europe.

In addition, the Reuters Institute for the Study of Journalism has demonstrated important trends in news consumption. It has shown major gains in reliance upon mobile news notifications. The percentage of people in the United States making use of this source has risen by 8 percentage points, while there have been gains of 7 percentage points in South Korea and 4 percentage points in Australia. There also have been increases in the use of news aggregators, digital news sources, and voice-activated digital assistants. 6

Declining trust in the news media

In the United States, there is a declining public trust in traditional journalism. The Gallup Poll asked a number of Americans over the past two decades how much trust and confidence they have in mass media reporting the news fully, accurately, and fairly. As shown in Figure 4, the percentage saying they had a great deal or fair amount of trust dropped from 53 percent in 1997 to 32 percent in 2016. 7

Between news coverage they don’t like and fake news that is manipulative in nature, many Americans question the accuracy of their news. A recent Gallup poll found that only 37 percent believe “news organizations generally get the facts straight.” This is down from about half of the country who felt that way in 1998. There is also a startling partisan divide in public assessments. Only 14 percent of Republicans believe the media report the news accurately, compared to 62 percent for Democrats. Even more disturbingly, “a solid majority of the country believes major news organizations routinely produce false information.” 8

This decline in public trust in media is dangerous for democracies. With the current political situation in a state of great flux in the U.S. and around the world, there are questions concerning the quality of the information available to the general public and the impact of marginal media organizations on voter assessments. These developments have complicated the manner in which people hold leaders accountable and the way in which our political system operates.

Challenges facing the digital media landscape

As the overall media landscape has changed, there have been several ominous developments. Rather than using digital tools to inform people and elevate civic discussion, some individuals have taken advantage of social and digital platforms to deceive, mislead, or harm others through creating or disseminating fake news and disinformation.

Fake news is generated by outlets that masquerade as actual media sites but promulgate false or misleading accounts designed to deceive the public. When these activities move from sporadic and haphazard to organized and systematic efforts, they become disinformation campaigns with the potential to disrupt campaigns and governance in entire countries. 9

As an illustration, the United States saw apparently organized efforts to disseminate false material in the 2016 presidential election. A Buzzfeed analysis found that the most widely shared fake news stories in 2016 were about “Pope Francis endorsing Donald Trump, Hillary Clinton selling weapons to ISIS, Hillary Clinton being disqualified from holding federal office, and the FBI director receiving millions from the Clinton Foundation.” 10 Using a social media assessment, it claimed that the 20 largest fake stories generated 8.7 million shares, reactions, and comments, compared to 7.4 million generated by the top 20 stories from 19 major news sites.

When [fake news] activities move from sporadic and haphazard to organized and systematic efforts, they become disinformation campaigns with the potential to disrupt campaigns and governance in entire countries.

Fake content was widespread during the presidential campaign. Facebook has estimated that 126 million of its platform users saw articles and posts promulgated by Russian sources. Twitter has found 2,752 accounts established by Russian groups that tweeted 1.4 million times in 2016. 11 The widespread nature of these disinformation efforts led Columbia Law School Professor Tim Wu to ask: “Did Twitter kill the First Amendment?” 12

A specific example of disinformation was the so-called “Pizzagate” conspiracy, which started on Twitter. The story falsely alleged that sexually abused children were hidden at Comet Ping Pong, a Washington, D.C. pizza parlor, and that Hillary Clinton knew about the sex ring. It seemed so realistic to some that a North Carolina man named Edgar Welch drove to the capital city with an assault weapon to personally search for the abused kids. After being arrested by the police, Welch said “that he had read online that the Comet restaurant was harboring child sex slaves and that he wanted to see for himself if they were there. [Welch] stated that he was armed.” 13

A post-election survey of 3,015 American adults suggested that it is difficult for news consumers to distinguish fake from real news. Chris Jackson of Ipsos Public Affairs undertook a survey that found “fake news headlines fool American adults about 75 percent of the time” and “‘fake news’ was remembered by a significant portion of the electorate and those stories were seen as credible.” 14 Another online survey of 1,200 individuals after the election by Hunt Allcott and Matthew Gentzkow found that half of those who saw these fake stories believed their content. 15

False news stories are not just a problem in the United States, but afflict other countries around the world. For example, India has been plagued by fake news concerning cyclones, public health, and child abuse. When intertwined with religious or caste issues, the combination can be explosive and lead to violence. People have been killed when false rumors have spread through digital media about child abductions. 16

Sometimes, fake news stories are amplified and disseminated quickly through false accounts, or automated “bots.” Most bots are benign in nature, and some major sites like Facebook ban bots and seek to remove them, but there are social bots that are “malicious entities designed specifically with the purpose to harm. These bots mislead, exploit, and manipulate social media discourse with rumors, spam, malware, misinformation, slander, or even just noise.” 17

This information can distort election campaigns, affect public perceptions, or shape human emotions. Recent research has found that “elusive bots could easily infiltrate a population of unaware humans and manipulate them to affect their perception of reality, with unpredictable results.” 18 In some cases, they can “engage in more complex types of interactions, such as entertaining conversations with other people, commenting on their posts, and answering their questions.” Through designated keywords and interactions with influential posters, they can magnify their influence and affect national or global conversations, especially resonating with like-minded clusters of people. 19

An analysis after the 2016 election found that automated bots played a major role in disseminating false information on Twitter. According to Jonathan Albright, an assistant professor of media analytics at Elon University, “what bots are doing is really getting this thing trending on Twitter. These bots are providing the online crowds that are providing legitimacy.” 20 With digital content, the more posts that are shared or liked, the more traffic they generate. Through these means, it becomes relatively easy to spread fake information over the internet. For example, as graphic content spreads, often with inflammatory comments attached, it can go viral and be seen as credible information by people far from the original post.

Everyone has a responsibility to combat the scourge of fake news. This ranges from supporting investigative journalism, reducing financial incentives for fake news, and improving digital literacy among the general public.

False information is dangerous because of its ability to affect public opinion and electoral discourse. According to David Lazer, “such situations can enable discriminatory and inflammatory ideas to enter public discourse and be treated as fact. Once embedded, such ideas can in turn be used to create scapegoats, to normalize prejudices, to harden us-versus-them mentalities and even, in extreme cases, to catalyze and justify violence.” 21  As he points out, factors such as source credibility, repetition, and social pressure affect information flows and the extent to which misinformation is taken seriously. When viewers see trusted sources repeat certain points, they are more likely to be influenced by that material.

Recent polling data demonstrate how harmful these practices have become to the reputations of reputable platforms. According to the Reuters Institute for the Study of Journalism, only 24 percent of Americans today believe social media sites “do a good job separating fact from fiction, compared to 40 percent for the news media.” 22 That demonstrates how much these developments have hurt public discourse.

The risks of regulation

Government harassment of journalists is a serious problem in many parts of the world. United Nations Human Rights Council Special Rapporteur David Kaye notes that “all too many leaders see journalism as the enemy, reporters as rogue actors, tweeps as terrorists, and bloggers as blasphemers.” 23  In Freedom House’s most recent report on global press freedoms, researchers found that media freedom was at its lowest point in 13 years and there were “unprecedented threats to journalists and media outlets in major democracies and new moves by authoritarian states to control the media, including beyond their borders.” 24

Journalists can often be accused of generating fake news and there have been numerous cases of legitimate journalists being arrested or their work being subject to official scrutiny. In Egypt, an Al-Jazeera producer was arrested on charges of “incitement against state institutions and broadcasting fake news with the aim of spreading chaos.” 25 This was after the network broadcast a documentary criticizing Egyptian military conscription.

Some governments have also moved to create government regulations to control information flows and censor content on social media platforms. Indonesia has established a government agency to “monitor news circulating online” and “tackle fake news.” 26 In the Philippines, Senator Joel Villanueva has introduced a bill that would impose up to a five-year prison term for those who publish or distribute “fake news,” which the legislation defined as activities that “cause panic, division, chaos, violence, and hate, or those which exhibit a propaganda to blacken or discredit one’s reputation.” 27

Critics have condemned the bill’s definition of social networks, misinformation, hate speech, and illegal speech as too broad, and believe that it risks criminalizing investigative journalism and limiting freedom of expression. Newspaper columnist Jarius Bondoc noted “the bill is prone to abuse. A bigot administration can apply it to suppress the opposition. By prosecuting critics as news fakers, the government can stifle legitimate dissent. Whistleblowers, not the grafters, would be imprisoned and fined for daring to talk. Investigative journalists would cram the jails.” 28

In a situation of false information, it is tempting for legal authorities to deal with offensive content and false news by forbidding or regulating it. For example, in Germany, legislation was passed in June 2017 that forces digital platforms to delete hate speech and misinformation. It requires large social media companies to “delete illegal, racist or slanderous comments and posts within 24 hours.” Companies can be fined up to $57 million for content that is not deleted from the platform, such as Nazi symbols, Holocaust denials, or language classified as hate speech. 29

The German legislation’s critics have complained that its definition of “obviously” illegal speech risks censorship and a loss of freedom of speech. As an illustration, the law applies the rules to social media platforms in the country with more than 2 million users. Commentators have noted that is not a reasonable way to define relevant social networks. There could be much smaller networks that inflict greater social damage.

Overly restrictive regulation of internet platforms in open societies sets a dangerous precedent and can encourage authoritarian regimes to continue and/or expand censorship.

In addition, it is not always clear how to identify objectionable content. 30 While it is pretty clear how to define speech advocating violence or harm to other people, it is less apparent when talking about hate speech or “defamation of the state.” What is considered “hateful” to one individual may not be to someone else. There is some ambiguity regarding what constitutes hate speech in a digital context. Does it include mistakes in reporting, opinion piece commentary, political satire, leader misstatements, or outright fabrications? Watchdog organizations complained that “overly broad language could affect a range of platforms and services and put decisions about what is illegal content into the hands of private companies that may be inclined to over-censor in order to avoid potential fines.” 31

Overly restrictive regulation of internet platforms in open societies sets a dangerous precedent and can encourage authoritarian regimes to continue and/or expand censorship. This will restrict global freedom of expression and generate hostility to democratic governance. Democracies that place undue limits on speech risk legitimizing authoritarian leaders and their efforts to crackdown basic human rights. It is crucial that efforts to improve news quality not weaken journalistic content or the investigative landscape facing reporters.

Other approaches

There are several alternatives to deal with falsehoods and disinformation that can be undertaken by various organizations. Many of these ideas represent solutions that combat fake news and disinformation without endangering freedom of expression and investigative journalism.

Government responsibilities

1) One of the most important thing governments around the world can do is to encourage independent, professional journalism . The general public needs reporters who help them make sense of complicated developments and deal with the ever-changing nature of social, economic, and political events. Many areas are going through transformation that I elsewhere have called “megachanges,” and these shifts have created enormous anger, anxiety, and confusion. 32 In a time of considerable turmoil, it is vital to have a healthy Fourth Estate that is independent of public authorities.

2) Governments should avoid crackdowns on the news media’s ability to cover the news. Those activities limit freedom of expression and hamper the ability of journalists to cover political developments. The United States should set a good example with other countries. If American leaders censor or restrict the news media, it encourages other countries to do the same thing.

3) Governments should avoid censoring content and making online platforms liable for misinformation. This could curb free expression, making people hesitant to share their political opinions for fear it could be censored as fake news. Such overly restrictive regulation could set a dangerous precedent and inadvertently encourage authoritarian regimes to weaken freedom of expression.

News industry actions

1) The news industry should continue to focus on high-quality journalism that builds trust and attracts greater audiences. An encouraging development is that many news organizations have experienced major gains in readership and viewership over the last couple of years, and this helps to put major news outlets on a better financial footing. But there have been precipitous drops in public confidence in the news media in recent years, and this has damaged the ability of journalists to report the news and hold leaders accountable. During a time of considerable chaos and disorder, the world needs a strong and viable news media that informs citizens about current events and long-term trends.

2) It is important for news organizations to call out fake news and disinformation without legitimizing them. They can do this by relying upon their in-house professionals and well-respected fact-checkers. In order to educate users about news sites that are created to mislead, nonprofit organizations such as Politifact, Factcheck.org, and Snopes judge the accuracy of leader claims and write stories detailing the truth or lack thereof of particular developments. These sources have become a visible part of election campaigns and candidate assessment in the United States and elsewhere. Research by Dartmouth College Professor Brendan Nyhan has found that labeling a Facebook post as “disputed” reduces the percentage of readers believing the false news by 10 percentage points. 33 In addition, Melissa Zimdars, a communication and media professor at Merrimack College, has created a list of 140 websites that use “distorted headlines and decontextualized or dubious information.” 34 This helps people track promulgators of false news.

It is important for news organizations to call out fake news and disinformation without legitimizing them.

Similar efforts are underway in other countries. In Ukraine, an organization known as StopFake relies upon “peer-to-peer counter propaganda” to dispel false stories. Its researchers assess “news stories for signs of falsified evidence, such as manipulated or misrepresented images and quotes” as well as looking for evidence of systematic misinformation campaigns. Over the past few years, it has found Russian social media posts alleging that Ukrainian military forces were engaging in atrocities against Russian nationalists living in eastern Ukraine or that they had swastikas painted on their vehicles. 35 In a related vein, the French news outlet Le Monde has a “database of more than 600 news sites that have been identified and tagged as ‘satire,’ ‘real,’ [or] ‘fake.’” 36

Crowdsourcing draws on the expertise of large numbers of readers or viewers to discern possible problems in news coverage, and it can be an effective way to deal with fake news. One example is The Guardian’s effort to draw on the wisdom of the crowd to assess 450,000 documents about Parliament member expenses in the United Kingdom. It received the documents but lacked the personnel quickly to analyze their newsworthiness. To deal with this situation, the newspaper created a public website that allowed ordinary people to read each document and designate it into one of four news categories: 1) “not interesting,” 2) “interesting but known,” 3) “interesting,” or 4) “investigate this.” 37 Digital platforms allow news organizations to engage large numbers of readers this way. The Guardian, for example, was able “to attract 20,000 readers to review 170,000 documents in the first 80 hours.” [38] These individuals helped the newspaper to assess which documents were most problematic and therefore worthy of further investigation and ultimately news coverage.

Technology company responsibilities

1) Technology firms should invest in technology to find fake news and identify it for users through algorithms and crowdsourcing . There are innovations in fake news and hoax detection that are useful to media platforms. For example, fake news detection can be automated, and social media companies should invest in their ability to do so. Former FCC Commissioner Tom Wheeler argues that “public interest algorithms” can aid in identifying and publicizing fake news posts and therefore be a valuable tool to protect consumers. 38

In this vein, computer scientist William Yang Wang, relying upon PolitiFact.com, created a public database of 12,836 statements labeled for accuracy and developed an algorithm that compared “surface-level linguistic patterns” from false assertions to wording contained in digital news stories. This allowed him to integrate text and analysis, and identify stories that rely on false information. His conclusion is that “when combining meta-data with text, significant improvements can be achieved for fine-grained fake news detection.” 39 In a similar approach, Eugenio Tacchini and colleagues say it is possible to identify hoaxes with a high degree of accuracy. Testing this proposition with a database of 15,500 Facebook posts and over 909,000 users, they find an accuracy rate of over 99 percent and say outside organizations can use their automatic tool to pinpoint sites engaging in fake news. 40 They use this result to advocate the development of automatic hoax detection systems.

Algorithms are powerful vehicles in the digital era and help shape people’s quest for information and how they find online material. They can also help with automatic hoax detection, and there are ways to identify fake news to educate readers without censoring it. According to Kelly Born of the William and Flora Hewlett Foundation, digital platforms should down rank or flag dubious stories, and find a way to better identify and rank authentic content to improve information-gathering and presentation. 41 As an example, several media platforms have instituted “disputed news” tags that warn readers and viewers about contentious content. This could be anything from information that is outright false to material where major parties disagree about its factualness. It is a way to warn readers about possible inaccuracies in online information. Wikipedia is another platform that does this. Since it publishes crowdsourced material, it is subject to competing claims regarding factual accuracy. It deals with this problem by adding tags to material identifying it as “disputed news.”

Yet this cannot be relied on by itself. A survey of 7,500 individuals undertaken by David Rand and Gordon Pennycook of Yale University argue that alerting readers about inaccurate information doesn’t help much. They explored the impact of independent fact-checkers and claim that “the existence of ‘disputed’ tags made participants just 3.7 percentage points more likely to correctly judge headlines as false.” 42 The authors worry that the outpouring of false news overwhelms fact-checkers and makes it impossible to evaluate disinformation.

Algorithms are powerful vehicles in the digital era, and they can help establish automatic hoax detection systems.

2) These companies shouldn’t make money from fake news manufacturers and should make it hard to monetize hoaxes . It is important to weaken financial incentives for bad content, especially false news and disinformation, as the manufacturing of fake news is often financially motivated. Like all clickbait, false information can be profitable due to ad revenues or general brand-building. Indeed, during the 2016 presidential campaign, trolls in countries such as Macedonia reported making a lot of money through their dissemination of erroneous material. While social media platforms like Facebook have made it harder for users to profit from fake news, 43 ad networks can do much more to stop the monetization of fake news, and publishers can stop carrying the ad networks that refuse to do so.

3) Strengthen online accountability through stronger real-name policies and enforcement against fake accounts. Firms can do this through “real-name registration,” which is the requirement that internet users have to provide the hosting platform with their true identity. This makes it easier to hold individuals accountable for what they post or disseminate online and also stops people from hiding behind fake names when they make offensive comments or engage in prohibited activities. 44 This is relevant to fake news and misinformation because of the likelihood that people will engage in worse behavior if they believe their actions are anonymous and not likely to be made public. As famed Justice Louis Brandeis long ago observed, “sunshine is said to be the best of disinfectants.” 45 It helps to keep people honest and accountable for their public activities.

Educational institutions

1) Funding efforts to enhance news literacy should be a high priority for governments. This is especially the case with people who are going online for the first time. For those individuals, it is hard to distinguish false from real news, and they need to learn how to evaluate news sources, not accept at face value everything they see on social media or digital news sites. Helping people become better consumers of online information is crucial as the world moves towards digital immersion. There should be money to support partnerships between journalists, businesses, educational institutions, and nonprofit organizations to encourage news literacy.

2) Education is especially important for young people . Research by Joseph Kahne and Benjamin Bowyer found that third-party assessments matter to young readers. However, their effects are limited. Those statements judged to be inaccurate reduced reader persuasion, although to a lower extent than alignment with the individual’s prior policy beliefs. 46 If the person already agreed with the statement, it was more difficult for fact-checking to sway them against the information.

How the public can protect itself

1) Individuals can protect themselves from false news and disinformation by following a diversity of people and perspectives . Relying upon a small number of like-minded news sources limits the range of material available to people and increases the odds they may fall victim to hoaxes or false rumors. This method is not entirely fool-proof, but it increases the odds of hearing well-balanced and diverse viewpoints.

2) In the online world, readers and viewers should be skeptical about news sources . In the rush to encourage clicks, many online outlets resort to misleading or sensationalized headlines. They emphasize the provocative or the attention-grabbing, even if that news hook is deceptive. News consumers have to keep their guard up and understand that not everything they read is accurate and many digital sites specialize in false news. Learning how to judge news sites and protect oneself from inaccurate information is a high priority in the digital age.

From this analysis, it is clear there are a number of ways to promote timely, accurate, and civil discourse in the face of false news and disinformation. 47 In today’s world, there is considerable experimentation taking place with online news platforms. News organizations are testing products and services that help them identify hate speech and language that incites violence. There is a major flowering of new models and approaches that bodes well for the future of online journalism and media consumption.

At the same time, everyone has a responsibility to combat the scourge of fake news and disinformation. This ranges from the promotion of strong norms on professional journalism, supporting investigative journalism, reducing financial incentives for fake news, and improving digital literacy among the general public. Taken together, these steps would further quality discourse and weaken the environment that has propelled disinformation around the globe.

Note: I wish to thank Hillary Schaub and Quinn Bornstein for their valuable research assistance. They were very helpful in finding useful materials for this project.

The Brookings Institution is a nonprofit organization devoted to independent research and policy solutions. Its mission is to conduct high-quality, independent research and, based on that research, to provide innovative, practical recommendations for policymakers and the public. The conclusions and recommendations of any Brookings publication are solely those of its author(s), and do not reflect the views of the Institution, its management, or its other scholars.

Support for this publication was generously provided by Facebook. Brookings recognizes that the value it provides is in its absolute commitment to quality, independence, and impact. Activities supported by its donors reflect this commitment.

• Pew Research Center, “Digital News Fact Sheet,” August 7, 2017.
• Pew Research Center, “How Americans Encounter, Recall, and Act Upon Digital News,” February 9, 2017.
• Pew Research Center, “More Than Half of Smartphone Users Get News Alerts, But Few Get Them Often,” September 8, 2016.
• Nic Newman, “Digital News Sources,” Reuters Institute for the Study of Journalism, 2017.
• Jacob Poushter, “Smartphone Ownership and Internet Usage Continues to Climb in Emerging Economies,” Pew Research Center, February 22, 2016.
• Gallup Poll, “Americans’ Trust in Mass Media Sinks to New Low,” September 14, 2016.
• Gallup Poll, “Republicans’, Democrats’ Views of Media Accuracy Diverge,” August 25, 2017.
• Jen Weedon, William Nuland, and Alex Stamos, “Information Operations,” Facebook, April 27, 2017.
• Craig Silverman, “This Analysis Shows How Viral Fake Election News Stories Outperformed Real News on Facebook,” BuzzFeedNews , November 16, 2016.
• Craig Timberg and Elizabeth Dwoskin, “Russian Content on Facebook, Google and Twitter Reached Far More Users Than Companies First Disclosed, Congressional Testimony Says,” Washington Post , October 30, 2017.
• Tim Wu, “Did Twitter Kill the First Amendment?”, New York Times , October 28, 2017, p. !a9.
• Marc Fisher, John Cox, and Peter Hermann, “Pizzagate: From Rumor, to Hashtag, to Gunfire in D.C.,” Washington Post , December 6, 2016.
• Craig Silverman and Jeremy Singer-Vine, “Most Americans Who See Fake News Believe It, New Survey Says,” BuzzFeed News , December 6, 2016.
• Hunt Allcott and Matthew Gentzkow, “Social Media and Fake News in the 2016 Election,” NBER Working Paper, April, 2017, p. 4.
• Vidhi Doshi, “India’s Millions of New Internet Users are Falling for Fake News – Sometimes with Deadly Consequences,” Washington Post , October 1, 2017.
• Emilio Ferrara, Onur Varol, Clayton Davis, Filippo Menczer, and Alessandro Flammini, “The Rise of Social Bots,” Communications of the ACM , July, 2016.
• Michela Del Vicario, Alessandro Bessi, Fabiana Zollo, Fabio Petroni, Antonio Scala, Guido Caldarelli, Eugene Stanley, and Walter Quattrociocchi, “The Spreading of Misinformation Online,” PNAS , January 19, 2016.
• David Lazer, Matthew Baum, Nir Grinberg, Lisa Friedland, Kenneth Joseph, Will Hobbs, and Carolina Mattsson, “Combating Fake News: An Agenda for Research and Action,” Harvard Shorenstein Center on Media, Politics and Public Policy and Harvard Ash Center for Democratic Governance and Innovation, May, 2017, p. 5.
• Office of the United Nations High Commissioner for Human Rights, “UN Expert Urges Governments to End ‘Demonization’ of Critical Media and Protect Journalists,” May 3, 2017.
• Freedom House, “Press Freedom’s Dark Horizon,” 2017.
• Committee to Protect Journalists, “Egypt Arrests Al-Jazeera Producer on Fake News Charge,” December 27, 2016.
• Straits Times , “Indonesia to Set Up Agency to Combat Fake News,” January 6, 2017.
• Mong Palatino, “Philippine Senator Moves to Criminalize ‘Fake News’ – Could This Lead to Censorship?”, Global Voices , July 7, 2017.
• Melissa Eddy and Mark Scott, “Delete Hate Speech or Pay Up, Germany Tells Social Media Companies,” New York Times , June 30, 2017.
• European Digital Rights, “Recommendations on the German Bill ‘Improving Law Enforcement on Social Networks’”, June 20, 2017.
• Courtney Radsch, “Proposed German Legislation Threatens Broad Internet Censorship,” Committee to Protect Journalists, April 20, 2017.
• Darrell M. West, Megachange: Economic Disruption, Political Upheaval, and Social Strife in the 21st Century , Brookings Institution Press, 2016.
• Brendan Nyhan, “Why the Fact-Checking at Facebook Needs to Be Checked,” New York Times , October 23, 2017.
• Kelly Born, “The Future of Truth: Can Philanthropy Help Mitigate Misinformation?”, William and Flora Hewlett Foundation, June 8, 2017 and Ananya Bhattacharya, “Here’s a Handy Cheat Sheet of False and Misleading ‘News’ Sites,” Quartz , November 17, 2016.
• Maria Haigh, Thomas Haigh, and Nadine Kozak, “Stopping Fake News: The Work Practices of Peer-to-Peer Counter Propaganda,” Journalist Studies , March 31, 2017.
• Kelly Born, “The Future of Truth: Can Philanthropy Help Mitigate Misinformation?”, William and Flora Hewlett Foundation, June 8, 2017.
• Reinhard Handler and Raul Conill, “Open Data, Crowdsouring and Game Mechanics: A Case Study on Civic Participation in the Digital Age,” Computer Supported Cooperative Work , 2016.
• Tom Wheeler, “Using ‘Public Interest Algorithms’ to Tackle the Problems Created by Social Media Algorithms,” Brookings TechTank, November 1, 2017.
• William Yang Wang, “’Liar, Liar Pants on Fire’, A New Benchmark Dataset for Fake News Detection”, Computation and Language , May, 2017.
• Eugenio Tacchini, Gabriele Ballarin, Marco Della Vedova, Stefano Moret, and Luca de Alfaro, “Some Like It Hoax: Automated Fake News Detection in Social Networks, Human-Computer Interaction , April 25, 2017.
• Jason Schwartz, “Study: Tagging Fake News on Facebook Doesn’t Work,” Politico , September 13, 2017, p. 19.
• Mike Isaac, “Facebook Mounts Effort to Limit Tide of Fake News,” New York Times , December 15, 2016.
• Zhixiong Liao, “An Economic Analysis on Internet Regulation in China and Proposals to Policy and Law Makers,” International Journal of Technology Policy and Law , 2016.
• Brainy Quote , “Louis Brandeis,” undated.
• Joseph Kahne and Benjamin Bowyer, “Educating for Democracy in a Partisan Age: Confronting the Challenges of Motivated Reasoning and Misinformation,” American Educational Research Journal , February, 2017.
• Darrell M. West and Beth Stone, “Nudging News Producers and Consumers Toward More Thoughtful, Less Polarized Discourse,” Brookings Institution Center for Effective Public Management, February, 2014.

Internet & Telecommunications Media & Journalism Social Media

Governance Studies

Center for Technology Innovation

Courtney C. Radsch

March 25, 2024

Valerie Wirtschafter

October 26, 2023

Jessica Brandt

May 5, 2023

• Follow us on Facebook
• Follow us on Twitter
• Criminal Justice
• Environment
• Politics & Government
• Race & Gender

Expert Commentary

Fake news and the spread of misinformation: A research roundup

This collection of research offers insights into the impacts of fake news and other forms of misinformation, including fake Twitter images, and how people use the internet to spread rumors and misinformation.

Republish this article

This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License .

by Denise-Marie Ordway, The Journalist's Resource September 1, 2017

This <a target="_blank" href="https://journalistsresource.org/politics-and-government/fake-news-conspiracy-theories-journalism-research/">article</a> first appeared on <a target="_blank" href="https://journalistsresource.org">The Journalist's Resource</a> and is republished here under a Creative Commons license.<img src="https://journalistsresource.org/wp-content/uploads/2020/11/cropped-jr-favicon-150x150.png" style="width:1em;height:1em;margin-left:10px;">

It’s too soon to say whether Google ’s and Facebook ’s attempts to clamp down on fake news will have a significant impact. But fabricated stories posing as serious journalism are not likely to go away as they have become a means for some writers to make money and potentially influence public opinion. Even as Americans recognize that fake news causes confusion about current issues and events, they continue to circulate it. A December 2016 survey by the Pew Research Center suggests that 23 percent of U.S. adults have shared fake news, knowingly or unknowingly, with friends and others.

“Fake news” is a term that can mean different things, depending on the context. News satire is often called fake news as are parodies such as the “Saturday Night Live” mock newscast Weekend Update. Much of the fake news that flooded the internet during the 2016 election season consisted of written pieces and recorded segments promoting false information or perpetuating conspiracy theories. Some news organizations published reports spotlighting examples of hoaxes, fake news and misinformation  on Election Day 2016.

The news media has written a lot about fake news and other forms of misinformation, but scholars are still trying to understand it — for example, how it travels and why some people believe it and even seek it out. Below, Journalist’s Resource has pulled together academic studies to help newsrooms better understand the problem and its impacts. Two other resources that may be helpful are the Poynter Institute’s tips on debunking fake news stories and the  First Draft Partner Network , a global collaboration of newsrooms, social media platforms and fact-checking organizations that was launched in September 2016 to battle fake news. In mid-2018, JR ‘s managing editor, Denise-Marie Ordway, wrote an article for  Harvard Business Review explaining what researchers know to date about the amount of misinformation people consume, why they believe it and the best ways to fight it.

—————————

“The Science of Fake News” Lazer, David M. J.; et al.   Science , March 2018. DOI: 10.1126/science.aao2998.

Summary: “The rise of fake news highlights the erosion of long-standing institutional bulwarks against misinformation in the internet age. Concern over the problem is global. However, much remains unknown regarding the vulnerabilities of individuals, institutions, and society to manipulations by malicious actors. A new system of safeguards is needed. Below, we discuss extant social and computer science research regarding belief in fake news and the mechanisms by which it spreads. Fake news has a long history, but we focus on unanswered scientific questions raised by the proliferation of its most recent, politically oriented incarnation. Beyond selected references in the text, suggested further reading can be found in the supplementary materials.”

“Who Falls for Fake News? The Roles of Bullshit Receptivity, Overclaiming, Familiarity, and Analytical Thinking” Pennycook, Gordon; Rand, David G. May 2018. Available at SSRN. DOI: 10.2139/ssrn.3023545.

Abstract:  “Inaccurate beliefs pose a threat to democracy and fake news represents a particularly egregious and direct avenue by which inaccurate beliefs have been propagated via social media. Here we present three studies (MTurk, N = 1,606) investigating the cognitive psychological profile of individuals who fall prey to fake news. We find consistent evidence that the tendency to ascribe profundity to randomly generated sentences — pseudo-profound bullshit receptivity — correlates positively with perceptions of fake news accuracy, and negatively with the ability to differentiate between fake and real news (media truth discernment). Relatedly, individuals who overclaim regarding their level of knowledge (i.e. who produce bullshit) also perceive fake news as more accurate. Conversely, the tendency to ascribe profundity to prototypically profound (non-bullshit) quotations is not associated with media truth discernment; and both profundity measures are positively correlated with willingness to share both fake and real news on social media. We also replicate prior results regarding analytic thinking — which correlates negatively with perceived accuracy of fake news and positively with media truth discernment — and shed further light on this relationship by showing that it is not moderated by the presence versus absence of information about the new headline’s source (which has no effect on perceived accuracy), or by prior familiarity with the news headlines (which correlates positively with perceived accuracy of fake and real news). Our results suggest that belief in fake news has similar cognitive properties to other forms of bullshit receptivity, and reinforce the important role that analytic thinking plays in the recognition of misinformation.”

“Social Media and Fake News in the 2016 Election” Allcott, Hunt; Gentzkow, Matthew. Working paper for the National Bureau of Economic Research, No. 23089, 2017.

Abstract: “We present new evidence on the role of false stories circulated on social media prior to the 2016 U.S. presidential election. Drawing on audience data, archives of fact-checking websites, and results from a new online survey, we find: (i) social media was an important but not dominant source of news in the run-up to the election, with 14 percent of Americans calling social media their “most important” source of election news; (ii) of the known false news stories that appeared in the three months before the election, those favoring Trump were shared a total of 30 million times on Facebook, while those favoring Clinton were shared eight million times; (iii) the average American saw and remembered 0.92 pro-Trump fake news stories and 0.23 pro-Clinton fake news stories, with just over half of those who recalled seeing fake news stories believing them; (iv) for fake news to have changed the outcome of the election, a single fake article would need to have had the same persuasive effect as 36 television campaign ads.”

“Debunking: A Meta-Analysis of the Psychological Efficacy of Messages Countering Misinformation” Chan, Man-pui Sally; Jones, Christopher R.; Jamieson, Kathleen Hall; Albarracín, Dolores. Psychological Science , September 2017. DOI: 10.1177/0956797617714579.

Abstract: “This meta-analysis investigated the factors underlying effective messages to counter attitudes and beliefs based on misinformation. Because misinformation can lead to poor decisions about consequential matters and is persistent and difficult to correct, debunking it is an important scientific and public-policy goal. This meta-analysis (k = 52, N = 6,878) revealed large effects for presenting misinformation (ds = 2.41–3.08), debunking (ds = 1.14–1.33), and the persistence of misinformation in the face of debunking (ds = 0.75–1.06). Persistence was stronger and the debunking effect was weaker when audiences generated reasons in support of the initial misinformation. A detailed debunking message correlated positively with the debunking effect. Surprisingly, however, a detailed debunking message also correlated positively with the misinformation-persistence effect.”

“Displacing Misinformation about Events: An Experimental Test of Causal Corrections” Nyhan, Brendan; Reifler, Jason. Journal of Experimental Political Science , 2015. doi: 10.1017/XPS.2014.22.

Abstract: “Misinformation can be very difficult to correct and may have lasting effects even after it is discredited. One reason for this persistence is the manner in which people make causal inferences based on available information about a given event or outcome. As a result, false information may continue to influence beliefs and attitudes even after being debunked if it is not replaced by an alternate causal explanation. We test this hypothesis using an experimental paradigm adapted from the psychology literature on the continued influence effect and find that a causal explanation for an unexplained event is significantly more effective than a denial even when the denial is backed by unusually strong evidence. This result has significant implications for how to most effectively counter misinformation about controversial political events and outcomes.”

“Rumors and Health Care Reform: Experiments in Political Misinformation” Berinsky, Adam J. British Journal of Political Science , 2015. doi: 10.1017/S0007123415000186.

Abstract: “This article explores belief in political rumors surrounding the health care reforms enacted by Congress in 2010. Refuting rumors with statements from unlikely sources can, under certain circumstances, increase the willingness of citizens to reject rumors regardless of their own political predilections. Such source credibility effects, while well known in the political persuasion literature, have not been applied to the study of rumor. Though source credibility appears to be an effective tool for debunking political rumors, risks remain. Drawing upon research from psychology on ‘fluency’ — the ease of information recall — this article argues that rumors acquire power through familiarity. Attempting to quash rumors through direct refutation may facilitate their diffusion by increasing fluency. The empirical results find that merely repeating a rumor increases its power.”

“Rumors and Factitious Informational Blends: The Role of the Web in Speculative Politics” Rojecki, Andrew; Meraz, Sharon. New Media & Society , 2016. doi: 10.1177/1461444814535724.

Abstract: “The World Wide Web has changed the dynamics of information transmission and agenda-setting. Facts mingle with half-truths and untruths to create factitious informational blends (FIBs) that drive speculative politics. We specify an information environment that mirrors and contributes to a polarized political system and develop a methodology that measures the interaction of the two. We do so by examining the evolution of two comparable claims during the 2004 presidential campaign in three streams of data: (1) web pages, (2) Google searches, and (3) media coverage. We find that the web is not sufficient alone for spreading misinformation, but it leads the agenda for traditional media. We find no evidence for equality of influence in network actors.”

“Analyzing How People Orient to and Spread Rumors in Social Media by Looking at Conversational Threads” Zubiaga, Arkaitz; et al. PLOS ONE, 2016. doi: 10.1371/journal.pone.0150989.

Abstract: “As breaking news unfolds people increasingly rely on social media to stay abreast of the latest updates. The use of social media in such situations comes with the caveat that new information being released piecemeal may encourage rumors, many of which remain unverified long after their point of release. Little is known, however, about the dynamics of the life cycle of a social media rumor. In this paper we present a methodology that has enabled us to collect, identify and annotate a dataset of 330 rumor threads (4,842 tweets) associated with 9 newsworthy events. We analyze this dataset to understand how users spread, support, or deny rumors that are later proven true or false, by distinguishing two levels of status in a rumor life cycle i.e., before and after its veracity status is resolved. The identification of rumors associated with each event, as well as the tweet that resolved each rumor as true or false, was performed by journalist members of the research team who tracked the events in real time. Our study shows that rumors that are ultimately proven true tend to be resolved faster than those that turn out to be false. Whilst one can readily see users denying rumors once they have been debunked, users appear to be less capable of distinguishing true from false rumors when their veracity remains in question. In fact, we show that the prevalent tendency for users is to support every unverified rumor. We also analyze the role of different types of users, finding that highly reputable users such as news organizations endeavor to post well-grounded statements, which appear to be certain and accompanied by evidence. Nevertheless, these often prove to be unverified pieces of information that give rise to false rumors. Our study reinforces the need for developing robust machine learning techniques that can provide assistance in real time for assessing the veracity of rumors. The findings of our study provide useful insights for achieving this aim.”

“Miley, CNN and The Onion” Berkowitz, Dan; Schwartz, David Asa. Journalism Practice , 2016. doi: 10.1080/17512786.2015.1006933.

Abstract: “Following a twerk-heavy performance by Miley Cyrus on the Video Music Awards program, CNN featured the story on the top of its website. The Onion — a fake-news organization — then ran a satirical column purporting to be by CNN’s Web editor explaining this decision. Through textual analysis, this paper demonstrates how a Fifth Estate comprised of bloggers, columnists and fake news organizations worked to relocate mainstream journalism back to within its professional boundaries.”

“Emotions, Partisanship, and Misperceptions: How Anger and Anxiety Moderate the Effect of Partisan Bias on Susceptibility to Political Misinformation”

Weeks, Brian E. Journal of Communication , 2015. doi: 10.1111/jcom.12164.

Abstract: “Citizens are frequently misinformed about political issues and candidates but the circumstances under which inaccurate beliefs emerge are not fully understood. This experimental study demonstrates that the independent experience of two emotions, anger and anxiety, in part determines whether citizens consider misinformation in a partisan or open-minded fashion. Anger encourages partisan, motivated evaluation of uncorrected misinformation that results in beliefs consistent with the supported political party, while anxiety at times promotes initial beliefs based less on partisanship and more on the information environment. However, exposure to corrections improves belief accuracy, regardless of emotion or partisanship. The results indicate that the unique experience of anger and anxiety can affect the accuracy of political beliefs by strengthening or attenuating the influence of partisanship.”

“Deception Detection for News: Three Types of Fakes” Rubin, Victoria L.; Chen, Yimin; Conroy, Niall J. Proceedings of the Association for Information Science and Technology , 2015, Vol. 52. doi: 10.1002/pra2.2015.145052010083.

Abstract: “A fake news detection system aims to assist users in detecting and filtering out varieties of potentially deceptive news. The prediction of the chances that a particular news item is intentionally deceptive is based on the analysis of previously seen truthful and deceptive news. A scarcity of deceptive news, available as corpora for predictive modeling, is a major stumbling block in this field of natural language processing (NLP) and deception detection. This paper discusses three types of fake news, each in contrast to genuine serious reporting, and weighs their pros and cons as a corpus for text analytics and predictive modeling. Filtering, vetting, and verifying online information continues to be essential in library and information science (LIS), as the lines between traditional news and online information are blurring.”

“When Fake News Becomes Real: Combined Exposure to Multiple News Sources and Political Attitudes of Inefficacy, Alienation, and Cynicism” Balmas, Meital. Communication Research , 2014, Vol. 41. doi: 10.1177/0093650212453600.

Abstract: “This research assesses possible associations between viewing fake news (i.e., political satire) and attitudes of inefficacy, alienation, and cynicism toward political candidates. Using survey data collected during the 2006 Israeli election campaign, the study provides evidence for an indirect positive effect of fake news viewing in fostering the feelings of inefficacy, alienation, and cynicism, through the mediator variable of perceived realism of fake news. Within this process, hard news viewing serves as a moderator of the association between viewing fake news and their perceived realism. It was also demonstrated that perceived realism of fake news is stronger among individuals with high exposure to fake news and low exposure to hard news than among those with high exposure to both fake and hard news. Overall, this study contributes to the scientific knowledge regarding the influence of the interaction between various types of media use on political effects.”

“Faking Sandy: Characterizing and Identifying Fake Images on Twitter During Hurricane Sandy” Gupta, Aditi; Lamba, Hemank; Kumaraguru, Ponnurangam; Joshi, Anupam. Proceedings of the 22nd International Conference on World Wide Web , 2013. doi: 10.1145/2487788.2488033.

Abstract: “In today’s world, online social media plays a vital role during real world events, especially crisis events. There are both positive and negative effects of social media coverage of events. It can be used by authorities for effective disaster management or by malicious entities to spread rumors and fake news. The aim of this paper is to highlight the role of Twitter during Hurricane Sandy (2012) to spread fake images about the disaster. We identified 10,350 unique tweets containing fake images that were circulated on Twitter during Hurricane Sandy. We performed a characterization analysis, to understand the temporal, social reputation and influence patterns for the spread of fake images. Eighty-six percent of tweets spreading the fake images were retweets, hence very few were original tweets. Our results showed that the top 30 users out of 10,215 users (0.3 percent) resulted in 90 percent of the retweets of fake images; also network links such as follower relationships of Twitter, contributed very little (only 11 percent) to the spread of these fake photos URLs. Next, we used classification models, to distinguish fake images from real images of Hurricane Sandy. Best results were obtained from Decision Tree classifier, we got 97 percent accuracy in predicting fake images from real. Also, tweet-based features were very effective in distinguishing fake images tweets from real, while the performance of user-based features was very poor. Our results showed that automated techniques can be used in identifying real images from fake images posted on Twitter.”

“The Impact of Real News about ‘Fake News’: Intertextual Processes and Political Satire” Brewer, Paul R.; Young, Dannagal Goldthwaite; Morreale, Michelle. International Journal of Public Opinion Research , 2013. doi: 10.1093/ijpor/edt015.

Abstract: “This study builds on research about political humor, press meta-coverage, and intertextuality to examine the effects of news coverage about political satire on audience members. The analysis uses experimental data to test whether news coverage of Stephen Colbert’s Super PAC influenced knowledge and opinion regarding Citizens United, as well as political trust and internal political efficacy. It also tests whether such effects depended on previous exposure to The Colbert Report (Colbert’s satirical television show) and traditional news. Results indicate that exposure to news coverage of satire can influence knowledge, opinion, and political trust. Additionally, regular satire viewers may experience stronger effects on opinion, as well as increased internal efficacy, when consuming news coverage about issues previously highlighted in satire programming.”

“With Facebook, Blogs, and Fake News, Teens Reject Journalistic ‘Objectivity’” Marchi, Regina. Journal of Communication Inquiry , 2012. doi: 10.1177/0196859912458700.

Abstract: “This article examines the news behaviors and attitudes of teenagers, an understudied demographic in the research on youth and news media. Based on interviews with 61 racially diverse high school students, it discusses how adolescents become informed about current events and why they prefer certain news formats to others. The results reveal changing ways news information is being accessed, new attitudes about what it means to be informed, and a youth preference for opinionated rather than objective news. This does not indicate that young people disregard the basic ideals of professional journalism but, rather, that they desire more authentic renderings of them.”

Keywords: alt-right, credibility, truth discovery, post-truth era, fact checking, news sharing, news literacy, misinformation, disinformation

5 fascinating digital media studies from fall 2018

Facebook and the newsroom: 6 questions for Siva Vaidhyanathan

About The Author

Denise-Marie Ordway

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Springer Nature - PMC COVID-19 Collection

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

Esma aïmeur.

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

Sabrine Amri

Gilles brassard, associated data.

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

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.

Introduction

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 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, 2 that the Earth was flat, 3 that aliens had invaded us, 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, 5 while in 2018, only one-fifth of them say they often get news via social media. 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. 7 For example, holding your breath for ten seconds to one minute is not a self-test for COVID-19 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 9 as fake and in some cases as dangerous and will never cure the infection.

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

Social media outperformed television as the major news source for young people of the UK and the USA. 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. 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. 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 13 and in 2018 14 as well as by the Collins dictionary in 2017. 15 , 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.

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. 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. 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.

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 .

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.

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?

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, 19 IEEE Xplore, 20 Springer Link, 21 ScienceDirect, 22 Scopus, 23 ACM Digital Library. 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.

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.

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 .

List of keywords for searching relevant articles

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.

Inclusion and exclusion criteria

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.

Classification of fake news definitions based on the used term and features

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 ).

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. 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. 26 A Google Trends Analysis of the term “fake news” reveals an explosion in popularity around the time of the 2016 US presidential election. 27 Fake news detection is a problem that has recently been addressed by numerous organizations, including the European Union 28 and NATO. 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).

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, 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.

A comparison between used terms based on intent and authenticity

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

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.

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.

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 ).

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.

A comparison between the different types of intent-based fake news

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 ).

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.

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.

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 ).

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).

Humans are the weakest factor due to the lack of awareness

Recent statistics 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 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.

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. 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.

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.

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.

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.

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

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.

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

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.

The features and datasets used in the news content-based approaches

a https://www.kaggle.com/anthonyc1/gathering-real-news-for-oct-dec-2016 , last access date: 26-12-2022

b https://mediabiasfactcheck.com/ , last access date: 26-12-2022

c https://github.com/KaiDMML/FakeNewsNet , last access date: 26-12-2022

d https://www.kaggle.com/anthonyc1/gathering-real-news-for-oct-dec-2016 , last access date: 26-12-2022

e https://www.cs.ucsb.edu/~william/data/liar_dataset.zip , last access date: 26-12-2022

f https://www.kaggle.com/mrisdal/fake-news , last access date: 26-12-2022

g https://github.com/BuzzFeedNews/2016-10-facebook-fact-check , last access date: 26-12-2022

h https://www.politifact.com/subjects/fake-news/ , last access date: 26-12-2022

i https://www.kaggle.com/rchitic17/real-or-fake , last access date: 26-12-2022

j https://www.kaggle.com/jruvika/fake-news-detection , last access date: 26-12-2022

k https://github.com/MKLab-ITI/image-verification-corpus , last access date: 26-12-2022

l https://drive.google.com/file/d/14VQ7EWPiFeGzxp3XC2DeEHi-BEisDINn/view , last access date: 26-12-2022

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.

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

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.

The features, detection cues and datasets used int the social context-based approaches

a https://www.dropbox.com/s/7ewzdrbelpmrnxu/rumdetect2017.zip , last access date: 26-12-2022 b https://snap.stanford.edu/data/ego-Twitter.html , last access date: 26-12-2022

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.

The features and datasets used in the hybrid approaches

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.

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 34 started addressing false information through independent fact-checkers in 2017, followed by Google 35 the same year. Two years later, Instagram 36 followed suit. However, the usefulness of fact-checking initiatives is questioned by journalists 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, 38 snopes.com, 39 Reuters, 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.

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.

Comparison of AI-based fake news detection techniques

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 ( ✓ ) 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).

A classification of popular blockchain-based approaches for fake news detection in social media

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.

Fake news detection approaches classification

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.

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.

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.

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.

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%).

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 .

Author 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.

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

Availability of data and material

Declarations.

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

1 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.

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

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

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

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

6 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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

27 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.

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

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

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

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

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

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

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

35 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.

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

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

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

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

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

Publisher's Note

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

Contributor Information

Esma Aïmeur, Email: ac.laertnomu.ori@ruemia .

Sabrine Amri, Email: [email protected] .

Gilles Brassard, Email: ac.laertnomu.ori@drassarb .

• 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 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 10.1109/AISP48273.2020.9073583
• Abu Arqoub O, Abdulateef Elega A, Efe Özad B, Dwikat H, Adedamola Oloyede F. Mapping the scholarship of fake news research: a systematic review. J Pract. 2022; 16 (1):56–86. doi: 10.1080/17512786.2020.1805791. [ CrossRef ] [ Google Scholar ]
• Ahmed S, Hinkelmann K, Corradini F. Development of fake news model using machine learning through natural language processing. Int J Comput Inf Eng. 2020; 14 (12):454–460. [ Google Scholar ]
• Aïmeur E, Brassard G, Rioux J. Data privacy: an end-user perspective. Int J Comput Netw Commun Secur. 2013; 1 (6):237–250. [ Google Scholar ]
• 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 10.1109/CSCI46756.2018.00244
• Alemanno A. How to counter fake news? A taxonomy of anti-fake news approaches. Eur J Risk Regul. 2018; 9 (1):1–5. doi: 10.1017/err.2018.12. [ CrossRef ] [ Google Scholar ]
• Allcott H, Gentzkow M. Social media and fake news in the 2016 election. J Econ Perspect. 2017; 31 (2):211–36. doi: 10.1257/jep.31.2.211. [ CrossRef ] [ Google Scholar ]
• Allen J, Howland B, Mobius M, Rothschild D, Watts DJ. Evaluating the fake news problem at the scale of the information ecosystem. Sci Adv. 2020 doi: 10.1126/sciadv.aay3539. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Allington D, Duffy B, Wessely S, Dhavan N, Rubin J. Health-protective behaviour, social media usage and conspiracy belief during the Covid-19 public health emergency. Psychol Med. 2020 doi: 10.1017/S003329172000224X. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• 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 10.37757/MR2020.V22.N2.12 [ PubMed ] [ CrossRef ]
• Altay S, Hacquin AS, Mercier H. Why do so few people share fake news? It hurts their reputation. New Media Soc. 2022; 24 (6):1303–1324. doi: 10.1177/1461444820969893. [ CrossRef ] [ Google Scholar ]
• 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. 10.1109/IJCNN48605.2020.9206973
• Andersen J, Søe SO. Communicative actions we live by: the problem with fact-checking, tagging or flagging fake news-the case of Facebook. Eur J Commun. 2020; 35 (2):126–139. doi: 10.1177/0267323119894489. [ CrossRef ] [ Google Scholar ]
• 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. doi: 10.1016/j.tele.2020.101475. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Apuke OD, Omar B, Tunca EA, Gever CV. The effect of visual multimedia instructions against fake news spread: a quasi-experimental study with Nigerian students. J Librariansh Inf Sci. 2022 doi: 10.1177/09610006221096477. [ CrossRef ] [ Google Scholar ]
• Aswani R, Ghrera S, Kar AK, Chandra S. Identifying buzz in social media: a hybrid approach using artificial bee colony and k-nearest neighbors for outlier detection. Soc Netw Anal Min. 2017; 7 (1):1–10. doi: 10.1007/s13278-017-0461-2. [ CrossRef ] [ Google Scholar ]
• Avram M, Micallef N, Patil S, Menczer F (2020) Exposure to social engagement metrics increases vulnerability to misinformation. arXiv preprint arxiv:2005.04682 , 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 10.1145/3308560.3316494
• Bahad P, Saxena P, Kamal R. Fake news detection using bi-directional LSTM-recurrent neural network. Procedia Comput Sci. 2019; 165 :74–82. doi: 10.1016/j.procs.2020.01.072. [ CrossRef ] [ Google Scholar ]
• 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 10.1109/SocialSens.2018.00009
• Balmas M. When fake news becomes real: combined exposure to multiple news sources and political attitudes of inefficacy, alienation, and cynicism. Commun Res. 2014; 41 (3):430–454. doi: 10.1177/0093650212453600. [ CrossRef ] [ Google Scholar ]
• Baptista JP, Gradim A. Understanding fake news consumption: a review. Soc Sci. 2020 doi: 10.3390/socsci9100185. [ CrossRef ] [ Google Scholar ]
• Baptista JP, Gradim A. A working definition of fake news. Encyclopedia. 2022; 2 (1):632–645. doi: 10.3390/encyclopedia2010043. [ CrossRef ] [ Google Scholar ]
• Bastick Z. Would you notice if fake news changed your behavior? An experiment on the unconscious effects of disinformation. Comput Hum Behav. 2021; 116 :106633. doi: 10.1016/j.chb.2020.106633. [ CrossRef ] [ Google Scholar ]
• Batailler C, Brannon SM, Teas PE, Gawronski B. A signal detection approach to understanding the identification of fake news. Perspect Psychol Sci. 2022; 17 (1):78–98. doi: 10.1177/1745691620986135. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bessi A, Ferrara E (2016) Social bots distort the 2016 US presidential election online discussion. First Monday 21(11-7). 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. Investigating differences in crowdsourced news credibility assessment: raters, tasks, and expert criteria. Proc ACM Hum Comput Interact. 2020; 4 (CSCW2):1–26. doi: 10.1145/3415164. [ CrossRef ] [ Google Scholar ]
• Bode L, Vraga EK. In related news, that was wrong: the correction of misinformation through related stories functionality in social media. J Commun. 2015; 65 (4):619–638. doi: 10.1111/jcom.12166. [ CrossRef ] [ Google Scholar ]
• Bondielli A, Marcelloni F. A survey on fake news and rumour detection techniques. Inf Sci. 2019; 497 :38–55. doi: 10.1016/j.ins.2019.05.035. [ CrossRef ] [ Google Scholar ]
• Bovet A, Makse HA. Influence of fake news in Twitter during the 2016 US presidential election. Nat Commun. 2019; 10 (1):1–14. doi: 10.1038/s41467-018-07761-2. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Brashier NM, Pennycook G, Berinsky AJ, Rand DG. Timing matters when correcting fake news. Proc Natl Acad Sci. 2021 doi: 10.1073/pnas.2020043118. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Brewer PR, Young DG, Morreale M. The impact of real news about “fake news”: intertextual processes and political satire. Int J Public Opin Res. 2013; 25 (3):323–343. doi: 10.1093/ijpor/edt015. [ CrossRef ] [ Google Scholar ]
• Bringula RP, Catacutan-Bangit AE, Garcia MB, Gonzales JPS, Valderama AMC. “Who is gullible to political disinformation?” Predicting susceptibility of university students to fake news. J Inf Technol Polit. 2022; 19 (2):165–179. doi: 10.1080/19331681.2021.1945988. [ CrossRef ] [ Google Scholar ]
• 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. 10.1007/978-3-319-60131-1_24
• Burshtein S. The true story on fake news. Intell Prop J. 2017; 29 (3):397–446. [ Google Scholar ]
• 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. 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 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. 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. How fake news differs from personal lies. Am Behav Sci. 2021; 65 (2):243–258. doi: 10.1177/0002764220910243. [ CrossRef ] [ Google Scholar ]
• Chung M, Kim N. When I learn the news is false: how fact-checking information stems the spread of fake news via third-person perception. Hum Commun Res. 2021; 47 (1):1–24. doi: 10.1093/hcr/hqaa010. [ CrossRef ] [ Google Scholar ]
• Clarke J, Chen H, Du D, Hu YJ. Fake news, investor attention, and market reaction. Inf Syst Res. 2020 doi: 10.1287/isre.2019.0910. [ CrossRef ] [ Google Scholar ]
• Clayton K, Blair S, Busam JA, Forstner S, Glance J, Green G, Kawata A, Kovvuri A, Martin J, Morgan E, et al. 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. 2020; 42 (4):1073–1095. doi: 10.1007/s11109-019-09533-0. [ CrossRef ] [ Google Scholar ]
• 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 10.1007/978-981-15-3380-8_49
• Conroy NK, Rubin VL, Chen Y. Automatic deception detection: methods for finding fake news. Proc Assoc Inf Sci Technol. 2015; 52 (1):1–4. doi: 10.1002/pra2.2015.145052010082. [ CrossRef ] [ Google Scholar ]
• Cooke NA. Posttruth, truthiness, and alternative facts: Information behavior and critical information consumption for a new age. Libr Q. 2017; 87 (3):211–221. doi: 10.1086/692298. [ CrossRef ] [ Google Scholar ]
• Coscia M, Rossi L. Distortions of political bias in crowdsourced misinformation flagging. J R Soc Interface. 2020; 17 (167):20200020. doi: 10.1098/rsif.2020.0020. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Dame Adjin-Tettey T. Combating fake news, disinformation, and misinformation: experimental evidence for media literacy education. Cogent Arts Human. 2022; 9 (1):2037229. doi: 10.1080/23311983.2022.2037229. [ CrossRef ] [ Google Scholar ]
• Deepak S, Chitturi B. Deep neural approach to fake-news identification. Procedia Comput Sci. 2020; 167 :2236–2243. doi: 10.1016/j.procs.2020.03.276. [ CrossRef ] [ Google Scholar ]
• 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. Polarization and fake news: early warning of potential misinformation targets. ACM Trans Web (TWEB) 2019; 13 (2):1–22. doi: 10.1145/3316809. [ CrossRef ] [ Google Scholar ]
• Demuyakor J, Opata EM. Fake news on social media: predicting which media format influences fake news most on facebook. J Intell Commun. 2022 doi: 10.54963/jic.v2i1.56. [ CrossRef ] [ Google Scholar ]
• 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. Misinformation and disinformation: the potential disadvantages of social media in infectious disease and how to combat them. Clin Infect Dis. 2022; 74 (Supplement–3):e34–e39. doi: 10.1093/cid/ciac109. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Di Domenico G, Sit J, Ishizaka A, Nunan D. Fake news, social media and marketing: a systematic review. J Bus Res. 2021; 124 :329–341. doi: 10.1016/j.jbusres.2020.11.037. [ CrossRef ] [ Google Scholar ]
• Dias N, Pennycook G, Rand DG. Emphasizing publishers does not effectively reduce susceptibility to misinformation on social media. Harv Kennedy School Misinform Rev. 2020 doi: 10.37016/mr-2020-001. [ CrossRef ] [ Google Scholar ]
• 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, 10.1007/978-3-030-42699-6_14
• Douglas KM, Uscinski JE, Sutton RM, Cichocka A, Nefes T, Ang CS, Deravi F. Understanding conspiracy theories. Polit Psychol. 2019; 40 :3–35. doi: 10.1111/pops.12568. [ CrossRef ] [ Google Scholar ]
• Edgerly S, Mourão RR, Thorson E, Tham SM. When do audiences verify? How perceptions about message and source influence audience verification of news headlines. J Mass Commun Q. 2020; 97 (1):52–71. doi: 10.1177/1077699019864680. [ CrossRef ] [ Google Scholar ]
• Egelhofer JL, Lecheler S. Fake news as a two-dimensional phenomenon: a framework and research agenda. Ann Int Commun Assoc. 2019; 43 (2):97–116. doi: 10.1080/23808985.2019.1602782. [ CrossRef ] [ Google Scholar ]
• 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, 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. New techniques to measure lie detection using Covid-19 fake news and the Multivariable Multiaxial Suggestibility Inventory-2 (MMSI-2) Comput Hum Behav Rep. 2021; 3 :100049. doi: 10.1016/j.chbr.2020.100049. [ CrossRef ] [ Google Scholar ]
• Fazio L. Pausing to consider why a headline is true or false can help reduce the sharing of false news. Harv Kennedy School Misinformation Rev. 2020 doi: 10.37016/mr-2020-009. [ CrossRef ] [ Google Scholar ]
• Ferrara E, Varol O, Davis C, Menczer F, Flammini A. The rise of social bots. Commun ACM. 2016; 59 (7):96–104. doi: 10.1145/2818717. [ CrossRef ] [ Google Scholar ]
• Flynn D, Nyhan B, Reifler J. The nature and origins of misperceptions: understanding false and unsupported beliefs about politics. Polit Psychol. 2017; 38 :127–150. doi: 10.1111/pops.12394. [ CrossRef ] [ Google Scholar ]
• Fraga-Lamas P, Fernández-Caramés TM. Fake news, disinformation, and deepfakes: leveraging distributed ledger technologies and blockchain to combat digital deception and counterfeit reality. IT Prof. 2020; 22 (2):53–59. doi: 10.1109/MITP.2020.2977589. [ CrossRef ] [ Google Scholar ]
• Freeman D, Waite F, Rosebrock L, Petit A, Causier C, East A, Jenner L, Teale AL, Carr L, Mulhall S, et al. Coronavirus conspiracy beliefs, mistrust, and compliance with government guidelines in England. Psychol Med. 2020 doi: 10.1017/S0033291720001890. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• 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. The impact of term fake news on the scientific community. Scientific performance and mapping in web of science. Soc Sci. 2020 doi: 10.3390/socsci9050073. [ CrossRef ] [ Google Scholar ]
• Garrett RK, Bond RM. Conservatives’ susceptibility to political misperceptions. Sci Adv. 2021 doi: 10.1126/sciadv.abf1234. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• 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 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, 10.1145/3201064.3201100
• Goldani MH, Momtazi S, Safabakhsh R. Detecting fake news with capsule neural networks. Appl Soft Comput. 2021; 101 :106991. doi: 10.1016/j.asoc.2020.106991. [ CrossRef ] [ Google Scholar ]
• Goldstein I, Yang L. Good disclosure, bad disclosure. J Financ Econ. 2019; 131 (1):118–138. doi: 10.1016/j.jfineco.2018.08.004. [ CrossRef ] [ Google Scholar ]
• Grinberg N, Joseph K, Friedland L, Swire-Thompson B, Lazer D. Fake news on Twitter during the 2016 US presidential election. Science. 2019; 363 (6425):374–378. doi: 10.1126/science.aau2706. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• 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 10.4018/978-1-7998-7291-7.ch013
• Guess A, Nagler J, Tucker J. Less than you think: prevalence and predictors of fake news dissemination on Facebook. Sci Adv. 2019 doi: 10.1126/sciadv.aau4586. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• 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. The future of false information detection on social media: new perspectives and trends. ACM Comput Surv (CSUR) 2020; 53 (4):1–36. doi: 10.1145/3393880. [ CrossRef ] [ Google Scholar ]
• Gupta A, Li H, Farnoush A, Jiang W. Understanding patterns of covid infodemic: a systematic and pragmatic approach to curb fake news. J Bus Res. 2022; 140 :670–683. doi: 10.1016/j.jbusres.2021.11.032. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ha L, Andreu Perez L, Ray R. Mapping recent development in scholarship on fake news and misinformation, 2008 to 2017: disciplinary contribution, topics, and impact. Am Behav Sci. 2021; 65 (2):290–315. doi: 10.1177/0002764219869402. [ CrossRef ] [ Google Scholar ]
• Habib A, Asghar MZ, Khan A, Habib A, Khan A. False information detection in online content and its role in decision making: a systematic literature review. Soc Netw Anal Min. 2019; 9 (1):1–20. doi: 10.1007/s13278-019-0595-5. [ CrossRef ] [ Google Scholar ]
• 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. 10.4018/978-1-7998-2543-2.ch014
• Hakak S, Alazab M, Khan S, Gadekallu TR, Maddikunta PKR, Khan WZ. An ensemble machine learning approach through effective feature extraction to classify fake news. Futur Gener Comput Syst. 2021; 117 :47–58. doi: 10.1016/j.future.2020.11.022. [ CrossRef ] [ Google Scholar ]
• 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. 10.1007/978-3-030-36987-3_17
• Hameleers M. 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. 2022; 25 (1):110–126. doi: 10.1080/1369118X.2020.1764603. [ CrossRef ] [ Google Scholar ]
• Hameleers M, Powell TE, Van Der Meer TG, Bos L. A picture paints a thousand lies? The effects and mechanisms of multimodal disinformation and rebuttals disseminated via social media. Polit Commun. 2020; 37 (2):281–301. doi: 10.1080/10584609.2019.1674979. [ CrossRef ] [ Google Scholar ]
• Hameleers M, Brosius A, de Vreese CH. Whom to trust? media exposure patterns of citizens with perceptions of misinformation and disinformation related to the news media. Eur J Commun. 2022 doi: 10.1177/02673231211072667. [ CrossRef ] [ Google Scholar ]
• Hartley K, Vu MK. Fighting fake news in the Covid-19 era: policy insights from an equilibrium model. Policy Sci. 2020; 53 (4):735–758. doi: 10.1007/s11077-020-09405-z. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hasan HR, Salah K. Combating deepfake videos using blockchain and smart contracts. IEEE Access. 2019; 7 :41596–41606. doi: 10.1109/ACCESS.2019.2905689. [ CrossRef ] [ Google Scholar ]
• 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 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. Fake news: a technological approach to proving the origins of content, using blockchains. Big Data. 2017; 5 (4):356–371. doi: 10.1089/big.2017.0071. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• 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 10.1145/3313831.3376375
• Ireton C, Posetti J. Journalism, fake news & disinformation: handbook for journalism education and training. Paris: UNESCO Publishing; 2018. [ Google Scholar ]
• Islam MR, Liu S, Wang X, Xu G. Deep learning for misinformation detection on online social networks: a survey and new perspectives. Soc Netw Anal Min. 2020; 10 (1):1–20. doi: 10.1007/s13278-020-00696-x. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ismailov M, Tsikerdekis M, Zeadally S. Vulnerabilities to online social network identity deception detection research and recommendations for mitigation. Fut Internet. 2020; 12 (9):148. doi: 10.3390/fi12090148. [ CrossRef ] [ Google Scholar ]
• 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. Cyberwar: how Russian hackers and trolls helped elect a president: what we don’t, can’t, and do know. Oxford: Oxford University Press; 2020. [ 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. 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, 10.1007/978-3-319-99007-1_88
• Jones-Jang SM, Mortensen T, Liu J. Does media literacy help identification of fake news? Information literacy helps, but other literacies don’t. Am Behav Sci. 2021; 65 (2):371–388. doi: 10.1177/0002764219869406. [ CrossRef ] [ Google Scholar ]
• Jungherr A, Schroeder R. Disinformation and the structural transformations of the public arena: addressing the actual challenges to democracy. Soc Media Soc. 2021 doi: 10.1177/2056305121988928. [ CrossRef ] [ Google Scholar ]
• 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 10.1109/CCAA.2018.8777343
• Kaliyar RK, Goswami A, Narang P, Sinha S. Fndnet—a deep convolutional neural network for fake news detection. Cogn Syst Res. 2020; 61 :32–44. doi: 10.1016/j.cogsys.2019.12.005. [ CrossRef ] [ Google Scholar ]
• Kapantai E, Christopoulou A, Berberidis C, Peristeras V. A systematic literature review on disinformation: toward a unified taxonomical framework. New Media Soc. 2021; 23 (5):1301–1326. doi: 10.1177/1461444820959296. [ CrossRef ] [ Google Scholar ]
• 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. Automating fake news detection system using multi-level voting model. Soft Comput. 2020; 24 (12):9049–9069. doi: 10.1007/s00500-019-04436-y. [ CrossRef ] [ Google Scholar ]
• 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. 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. 10.1145/3159652.3159734
• Klein D, Wueller J. Fake news: a legal perspective. J Internet Law. 2017; 20 (10):5–13. [ Google Scholar ]
• 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. Misinformation and the currency of democratic citizenship. J Polit. 2000; 62 (3):790–816. doi: 10.1111/0022-3816.00033. [ CrossRef ] [ Google Scholar ]
• 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. 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. 10.1007/978-3-030-45442-5_26
• Lanius C, Weber R, MacKenzie WI. Use of bot and content flags to limit the spread of misinformation among social networks: a behavior and attitude survey. Soc Netw Anal Min. 2021; 11 (1):1–15. doi: 10.1007/s13278-021-00739-x. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lazer DM, Baum MA, Benkler Y, Berinsky AJ, Greenhill KM, Menczer F, Metzger MJ, Nyhan B, Pennycook G, Rothschild D, et al. The science of fake news. Science. 2018; 359 (6380):1094–1096. doi: 10.1126/science.aao2998. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• 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. 10.1145/3341161.3342875
• Lewandowsky S (2020) Climate change, disinformation, and how to combat it. In: Annual Review of Public Health 42. 10.1146/annurev-publhealth-090419-102409 [ PubMed ]
• 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. Credibility perceptions and detection accuracy of fake news headlines on social media: effects of truth-bias and endorsement cues. Commun Res. 2022; 49 (2):171–195. doi: 10.1177/0093650220921321. [ CrossRef ] [ Google Scholar ]
• Lutzke L, Drummond C, Slovic P, Árvai J. Priming critical thinking: simple interventions limit the influence of fake news about climate change on Facebook. Glob Environ Chang. 2019; 58 :101964. doi: 10.1016/j.gloenvcha.2019.101964. [ CrossRef ] [ Google Scholar ]
• Maertens R, Anseel F, van der Linden S. Combatting climate change misinformation: evidence for longevity of inoculation and consensus messaging effects. J Environ Psychol. 2020; 70 :101455. doi: 10.1016/j.jenvp.2020.101455. [ CrossRef ] [ Google Scholar ]
• Mahabub A. A robust technique of fake news detection using ensemble voting classifier and comparison with other classifiers. SN Applied Sciences. 2020; 2 (4):1–9. doi: 10.1007/s42452-020-2326-y. [ CrossRef ] [ Google Scholar ]
• Mahbub S, Pardede E, Kayes A, Rahayu W. Controlling astroturfing on the internet: a survey on detection techniques and research challenges. Int J Web Grid Serv. 2019; 15 (2):139–158. doi: 10.1504/IJWGS.2019.099561. [ CrossRef ] [ Google Scholar ]
• Marsden C, Meyer T, Brown I. Platform values and democratic elections: how can the law regulate digital disinformation? Comput Law Secur Rev. 2020; 36 :105373. doi: 10.1016/j.clsr.2019.105373. [ CrossRef ] [ Google Scholar ]
• 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. 10.1145/3410566.3410599
• Mazzeo V, Rapisarda A. Investigating fake and reliable news sources using complex networks analysis. Front Phys. 2022; 10 :886544. doi: 10.3389/fphy.2022.886544. [ CrossRef ] [ Google Scholar ]
• McGrew S. Learning to evaluate: an intervention in civic online reasoning. Comput Educ. 2020; 145 :103711. doi: 10.1016/j.compedu.2019.103711. [ CrossRef ] [ Google Scholar ]
• McGrew S, Breakstone J, Ortega T, Smith M, Wineburg S. Can students evaluate online sources? Learning from assessments of civic online reasoning. Theory Res Soc Educ. 2018; 46 (2):165–193. doi: 10.1080/00933104.2017.1416320. [ CrossRef ] [ Google Scholar ]
• Meel P, Vishwakarma DK. Fake news, rumor, information pollution in social media and web: a contemporary survey of state-of-the-arts, challenges and opportunities. Expert Syst Appl. 2020; 153 :112986. doi: 10.1016/j.eswa.2019.112986. [ CrossRef ] [ Google Scholar ]
• Meese J, Frith J, Wilken R. Covid-19, 5G conspiracies and infrastructural futures. Media Int Aust. 2020; 177 (1):30–46. doi: 10.1177/1329878X20952165. [ CrossRef ] [ Google Scholar ]
• Metzger MJ, Hartsell EH, Flanagin AJ. Cognitive dissonance or credibility? A comparison of two theoretical explanations for selective exposure to partisan news. Commun Res. 2020; 47 (1):3–28. doi: 10.1177/0093650215613136. [ CrossRef ] [ Google Scholar ]
• 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. Spreadable spectacle in digital culture: civic expression, fake news, and the role of media literacies in “post-fact society. Am Behav Sci. 2017; 61 (4):441–454. doi: 10.1177/0002764217701217. [ CrossRef ] [ Google Scholar ]
• 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. Analyzing machine learning enabled fake news detection techniques for diversified datasets. Wirel Commun Mobile Comput. 2022 doi: 10.1155/2022/1575365. [ CrossRef ] [ Google Scholar ]
• Molina MD, Sundar SS, Le T, Lee D. “Fake news” is not simply false information: a concept explication and taxonomy of online content. Am Behav Sci. 2021; 65 (2):180–212. doi: 10.1177/0002764219878224. [ CrossRef ] [ Google Scholar ]
• 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. 10.1145/3091478.3091523
• Nagel TW. Measuring fake news acumen using a news media literacy instrument. J Media Liter Educ. 2022; 14 (1):29–42. doi: 10.23860/JMLE-2022-14-1-3. [ CrossRef ] [ Google Scholar ]
• Nakov P (2020) Can we spot the “fake news” before it was even written? arXiv preprint arXiv:2008.04374
• Nekmat E. Nudge effect of fact-check alerts: source influence and media skepticism on sharing of news misinformation in social media. Soc Media Soc. 2020 doi: 10.1177/2056305119897322. [ CrossRef ] [ Google Scholar ]
• Nygren T, Brounéus F, Svensson G. Diversity and credibility in young people’s news feeds: a foundation for teaching and learning citizenship in a digital era. J Soc Sci Educ. 2019; 18 (2):87–109. doi: 10.4119/jsse-917. [ CrossRef ] [ Google Scholar ]
• Nyhan B, Reifler J. Displacing misinformation about events: an experimental test of causal corrections. J Exp Polit Sci. 2015; 2 (1):81–93. doi: 10.1017/XPS.2014.22. [ CrossRef ] [ Google Scholar ]
• Nyhan B, Porter E, Reifler J, Wood TJ. Taking fact-checks literally but not seriously? The effects of journalistic fact-checking on factual beliefs and candidate favorability. Polit Behav. 2020; 42 (3):939–960. doi: 10.1007/s11109-019-09528-x. [ CrossRef ] [ Google Scholar ]
• 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. 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. 10.1007/978-3-030-29238-6_8
• Ozbay FA, Alatas B. Fake news detection within online social media using supervised artificial intelligence algorithms. Physica A. 2020; 540 :123174. doi: 10.1016/j.physa.2019.123174. [ CrossRef ] [ Google Scholar ]
• 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. 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 436–441.10.1109/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. Investigating the emotional appeal of fake news using artificial intelligence and human contributions. J Prod Brand Manag. 2019; 29 (2):223–233. doi: 10.1108/JPBM-12-2018-2179. [ CrossRef ] [ Google Scholar ]
• 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. Astroturfing detection in social media: a binary n-gram-based approach. Concurr Comput: Pract Exp. 2017; 29 (17):e4013. doi: 10.1002/cpe.4013. [ CrossRef ] [ Google Scholar ]
• Pennycook G, Rand DG. Fighting misinformation on social media using crowdsourced judgments of news source quality. Proc Natl Acad Sci. 2019; 116 (7):2521–2526. doi: 10.1073/pnas.1806781116. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pennycook G, Rand DG. Who falls for fake news? The roles of bullshit receptivity, overclaiming, familiarity, and analytic thinking. J Pers. 2020; 88 (2):185–200. doi: 10.1111/jopy.12476. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pennycook G, Bear A, Collins ET, Rand DG. The implied truth effect: attaching warnings to a subset of fake news headlines increases perceived accuracy of headlines without warnings. Manag Sci. 2020; 66 (11):4944–4957. doi: 10.1287/mnsc.2019.3478. [ CrossRef ] [ Google Scholar ]
• Pennycook G, McPhetres J, Zhang Y, Lu JG, Rand DG. Fighting Covid-19 misinformation on social media: experimental evidence for a scalable accuracy-nudge intervention. Psychol Sci. 2020; 31 (7):770–780. doi: 10.1177/0956797620939054. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• 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. 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. 10.1609/aaai.v34i01.5386
• Qayyum A, Qadir J, Janjua MU, Sher F. Using blockchain to rein in the new post-truth world and check the spread of fake news. IT Prof. 2019; 21 (4):16–24. doi: 10.1109/MITP.2019.2910503. [ CrossRef ] [ Google Scholar ]
• 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. 10.24963/ijcai.2018/533
• Raza S, Ding C. Fake news detection based on news content and social contexts: a transformer-based approach. Int J Data Sci Anal. 2022; 13 (4):335–362. doi: 10.1007/s41060-021-00302-z. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• 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. Fake news game confers psychological resistance against online misinformation. Palgrave Commun. 2019; 5 (1):1–10. doi: 10.1057/s41599-019-0279-9. [ CrossRef ] [ Google Scholar ]
• Roozenbeek J, van der Linden S, Nygren T. Prebunking interventions based on the psychological theory of “inoculation” can reduce susceptibility to misinformation across cultures. Harv Kennedy School Misinformation Rev. 2020 doi: 10.37016//mr-2020-008. [ CrossRef ] [ Google Scholar ]
• Roozenbeek J, Schneider CR, Dryhurst S, Kerr J, Freeman AL, Recchia G, Van Der Bles AM, Van Der Linden S. Susceptibility to misinformation about Covid-19 around the world. R Soc Open Sci. 2020; 7 (10):201199. doi: 10.1098/rsos.201199. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• 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. 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. 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. 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. 10.1145/2872518.2890098
• Shao C, Ciampaglia GL, Varol O, Yang KC, Flammini A, Menczer F. The spread of low-credibility content by social bots. Nat Commun. 2018; 9 (1):1–9. doi: 10.1038/s41467-018-06930-7. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Shao C, Hui PM, Wang L, Jiang X, Flammini A, Menczer F, Ciampaglia GL. Anatomy of an online misinformation network. PLoS ONE. 2018; 13 (4):e0196087. doi: 10.1371/journal.pone.0196087. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sharma K, Qian F, Jiang H, Ruchansky N, Zhang M, Liu Y. Combating fake news: a survey on identification and mitigation techniques. ACM Trans Intell Syst Technol (TIST) 2019; 10 (3):1–42. doi: 10.1145/3305260. [ CrossRef ] [ Google Scholar ]
• 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. Fake images: the effects of source, intermediary, and digital media literacy on contextual assessment of image credibility online. New Media Soc. 2019; 21 (2):438–463. doi: 10.1177/1461444818799526. [ CrossRef ] [ Google Scholar ]
• Sherman IN, Redmiles EM, Stokes JW (2020) Designing indicators to combat fake media. arXiv preprint arXiv:2010.00544
• Shi P, Zhang Z, Choo KKR. Detecting malicious social bots based on clickstream sequences. IEEE Access. 2019; 7 :28855–28862. doi: 10.1109/ACCESS.2019.2901864. [ CrossRef ] [ Google Scholar ]
• Shu K, Sliva A, Wang S, Tang J, Liu H. Fake news detection on social media: a data mining perspective. ACM SIGKDD Explor Newsl. 2017; 19 (1):22–36. doi: 10.1145/3137597.3137600. [ CrossRef ] [ Google Scholar ]
• 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 , 10.1089/big.2020.0062 [ PubMed ]
• 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. 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. 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. 10.1145/3341161.3342927
• Shu K, Bhattacharjee A, Alatawi F, Nazer TH, Ding K, Karami M, Liu H. Combating disinformation in a social media age. Wiley Interdiscip Rev: Data Min Knowl Discov. 2020; 10 (6):e1385. doi: 10.1002/widm.1385. [ CrossRef ] [ Google Scholar ]
• Shu K, Mahudeswaran D, Wang S, Liu H. Hierarchical propagation networks for fake news detection: investigation and exploitation. Proc Int AAAI Conf Web Soc Media AAAI Press. 2020; 14 :626–637. [ Google Scholar ]
• 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 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. Detecting fake news stories via multimodal analysis. J Am Soc Inf Sci. 2021; 72 (1):3–17. doi: 10.1002/asi.24359. [ CrossRef ] [ Google Scholar ]
• 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. 10.14778/3358701.3358708 [ CrossRef ]
• 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 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 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. Conspiracy theories and the conspiracy mindset: implications for political ideology. Curr Opin Behav Sci. 2020; 34 :118–122. doi: 10.1016/j.cobeha.2020.02.015. [ CrossRef ] [ Google Scholar ]
• Tandoc EC, Jr, Thomas RJ, Bishop L. What is (fake) news? Analyzing news values (and more) in fake stories. Media Commun. 2021; 9 (1):110–119. doi: 10.17645/mac.v9i1.3331. [ CrossRef ] [ Google Scholar ]
• Tchakounté F, Faissal A, Atemkeng M, Ntyam A. A reliable weighting scheme for the aggregation of crowd intelligence to detect fake news. Information. 2020; 11 (6):319. doi: 10.3390/info11060319. [ CrossRef ] [ Google Scholar ]
• 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 10.1007/978-3-030-30796-7_20
• Treen KMd, Williams HT, O’Neill SJ. Online misinformation about climate change. Wiley Interdiscip Rev Clim Change. 2020; 11 (5):e665. doi: 10.1002/wcc.665. [ CrossRef ] [ Google Scholar ]
• Tsang SJ. Motivated fake news perception: the impact of news sources and policy support on audiences’ assessment of news fakeness. J Mass Commun Q. 2020 doi: 10.1177/1077699020952129. [ CrossRef ] [ Google Scholar ]
• 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. 10.1145/3184558.3188722
• Uppada SK, Manasa K, Vidhathri B, Harini R, Sivaselvan B. Novel approaches to fake news and fake account detection in OSNS: user social engagement and visual content centric model. Soc Netw Anal Min. 2022; 12 (1):1–19. doi: 10.1007/s13278-022-00878-9. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Van der Linden S, Roozenbeek J (2020) Psychological inoculation against fake news. In: Accepting, sharing, and correcting misinformation, the psychology of fake news. 10.4324/9780429295379-11
• Van der Linden S, Panagopoulos C, Roozenbeek J. You are fake news: political bias in perceptions of fake news. Media Cult Soc. 2020; 42 (3):460–470. doi: 10.1177/0163443720906992. [ CrossRef ] [ Google Scholar ]
• Valenzuela S, Muñiz C, Santos M. Social media and belief in misinformation in mexico: a case of maximal panic, minimal effects? Int J Press Polit. 2022 doi: 10.1177/19401612221088988. [ CrossRef ] [ Google Scholar ]
• 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. 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). 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. Detection and veracity analysis of fake news via scrapping and authenticating the web search. Cogn Syst Res. 2019; 58 :217–229. doi: 10.1016/j.cogsys.2019.07.004. [ CrossRef ] [ Google Scholar ]
• 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. 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. 10.1145/3394171.3414692
• Vosoughi S, Roy D, Aral S. The spread of true and false news online. Science. 2018; 359 (6380):1146–1151. doi: 10.1126/science.aap9559. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Vraga EK, Bode L. Using expert sources to correct health misinformation in social media. Sci Commun. 2017; 39 (5):621–645. doi: 10.1177/1075547017731776. [ CrossRef ] [ Google Scholar ]
• Waldman AE. The marketplace of fake news. Univ Pa J Const Law. 2017; 20 :845. [ Google Scholar ]
• 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. Understanding archetypes of fake news via fine-grained classification. Soc Netw Anal Min. 2019; 9 (1):1–17. doi: 10.1007/s13278-019-0580-z. [ CrossRef ] [ Google Scholar ]
• 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. 10.1007/978-3-030-23407-2_11
• Wang Y, McKee M, Torbica A, Stuckler D. Systematic literature review on the spread of health-related misinformation on social media. Soc Sci Med. 2019; 240 :112552. doi: 10.1016/j.socscimed.2019.112552. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• 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. 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. The need for smarter definitions and practical, timely empirical research on information disorder. Digit J. 2018; 6 (8):951–963. doi: 10.1080/21670811.2018.1502047. [ CrossRef ] [ Google Scholar ]
• Wardle C, Derakhshan H. Information disorder: toward an interdisciplinary framework for research and policy making. Council Eur Rep. 2017; 27 :1–107. [ Google Scholar ]
• Weiss AP, Alwan A, Garcia EP, Garcia J. Surveying fake news: assessing university faculty’s fragmented definition of fake news and its impact on teaching critical thinking. Int J Educ Integr. 2020; 16 (1):1–30. doi: 10.1007/s40979-019-0049-x. [ CrossRef ] [ Google Scholar ]
• 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. 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. Misinformation in social media: definition, manipulation, and detection. ACM SIGKDD Explor Newsl. 2019; 21 (2):80–90. doi: 10.1145/3373464.3373475. [ CrossRef ] [ Google Scholar ]
• Wu Y, Ngai EW, Wu P, Wu C. Fake news on the internet: a literature review, synthesis and directions for future research. Intern Res. 2022 doi: 10.1108/INTR-05-2021-0294. [ CrossRef ] [ Google Scholar ]
• Xu K, Wang F, Wang H, Yang B. Detecting fake news over online social media via domain reputations and content understanding. Tsinghua Sci Technol. 2019; 25 (1):20–27. doi: 10.26599/TST.2018.9010139. [ CrossRef ] [ Google Scholar ]
• 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. 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. 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. 10.1145/3313831.3376213
• Yavary A, Sajedi H, Abadeh MS. Information verification in social networks based on user feedback and news agencies. Soc Netw Anal Min. 2020; 10 (1):1–8. doi: 10.1007/s13278-019-0616-4. [ CrossRef ] [ Google Scholar ]
• Yazdi KM, Yazdi AM, Khodayi S, Hou J, Zhou W, Saedy S. Improving fake news detection using k-means and support vector machine approaches. Int J Electron Commun Eng. 2020; 14 (2):38–42. doi: 10.5281/zenodo.3669287. [ CrossRef ] [ Google Scholar ]
• Zannettou S, Sirivianos M, Blackburn J, Kourtellis N. The web of false information: rumors, fake news, hoaxes, clickbait, and various other shenanigans. J Data Inf Qual (JDIQ) 2019; 11 (3):1–37. doi: 10.1145/3309699. [ CrossRef ] [ Google Scholar ]
• 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. An overview of online fake news: characterization, detection, and discussion. Inf Process Manag. 2020; 57 (2):102025. doi: 10.1016/j.ipm.2019.03.004. [ CrossRef ] [ Google Scholar ]
• 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. 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 10.1109/WIFS47025.2019.9035107
• Zhou X, Zafarani R. A survey of fake news: fundamental theories, detection methods, and opportunities. ACM Comput Surv (CSUR) 2020; 53 (5):1–40. doi: 10.1145/3395046. [ CrossRef ] [ Google Scholar ]
• Zubiaga A, Aker A, Bontcheva K, Liakata M, Procter R. Detection and resolution of rumours in social media: a survey. ACM Comput Surv (CSUR) 2018; 51 (2):1–36. doi: 10.1145/3161603. [ CrossRef ] [ Google Scholar ]

• Contributors
• Valuing Black Lives
• Black Issues in Philosophy
• Blog Announcements
• Climate Matters
• Genealogies of Philosophy
• Graduate Student Council (GSC)
• Graduate Student Reflection
• Into Philosophy
• Member Interviews
• On Congeniality
• Philosophy as a Way of Life
• Philosophy in the Contemporary World
• Precarity and Philosophy
• Recently Published Book Spotlight
• Starting Out in Philosophy
• Syllabus Showcase
• Teaching and Learning Video Series
• Undergraduate Philosophy Club
• Women in Philosophy
• Diversity and Inclusiveness
• Issues in Philosophy
• Public Philosophy
• Work/Life Balance
• Submissions
• Journal Surveys
• APA Connect

Four Theses on Fake News

Fake news undermines free speech culture by impairing our ability to develop and express our thoughts. To fix the problem, w e need to police intent rather than content.

Was Twitter right to ban former President Trump for spreading lies about election fraud? Should Representative Marjorie Taylor Greene have been stripped of her committee roles? Did the Parler app deserve to be shut down for providing a platform to echo all those lies? And what should we do about Facebook, the Death Star of fake news?

We are struggling to answer these questions. A big reason why is that we still do not have a clear understanding of what fake news is, why it is bad, and how we can fix it. Here are four theses that might be of some help:

1.) Fake News is not Free Speech

Fake news requires the intent to deceive others about some current event or issue. It is speech produced by a person or organization who does not believe what the speech conveys, and yet they intend to convince others of its truth. This is why not all false news is fake news. People may accidentally say untrue or misleading things, but they are not thereby generating fake news.

What, then, is wrong with fake news? The problem is not just that a few liars are ruining social media feeds. The deeper problem is that fake news undermines our free speech culture. That may initially seem flat out false: after all, fake news is an exercise of free speech, not an abridgment of it. But that is not the case. To see why, we need to appreciate the moral reasons for protecting freedom of speech.

Freedom of speech gives us the ability to think and speak freely. As UCLA Professor Seana Shiffrin argues, we are morally justified in protecting the freedom of speech because it is necessary for us to live flourishing human lives. Developing and expressing our thoughts is an essential part of living well, and freedom of speech creates the environment in which that is possible. Freedom of speech opens the so-called marketplace of ideas where we come to understand the world and our place in it. Without free speech culture, our lives would be truly impoverished.

Fake news undermines our free speech culture because it impairs our ability to develop and express our thoughts. It does so by polluting public discourse with speech that is deliberately deceptive. In such an environment, sincere speech is not only harder to come by, but also harder to trust. It is more difficult for us to believe and to be believed. And, as Hannah Arendt points out, this imperils our capacity to think: “a people that no longer can believe anything cannot make up its mind. It is deprived not only of its capacity to act but also of its capacity to think and to judge.”

2.) Fake is Worse Than False

Fake news is likely worse than misinformation in two respects. First, the fake news uttered from some soapbox will often reverberate through the echo chambers until it comes out as something no longer just said, but believed. A lie from the Rose Garden becomes gospel at the dinner table. Second, and more importantly, fake news has a much greater corroding effect on free speech culture. Americans worry not so much that the media are accidentally wrong, but that they are willfully biased. According to a recent poll by Gallup and the Knight Foundation,  “Americans perceive inaccurate news to be intentional – either because the reporter is misrepresenting the facts (52%) or making them up entirely (28%).” While every society can tolerate some degree of insincerity and deception, in America the well of trust has become almost unpotable.

3.) Police Intent, not Content

How, then, do we fix the problem of fake news? We need to police intent rather than content. We do that by authorizing agencies and institutions to regulate and disincentivize deceptive information masquerading as news. Whether those agencies are governmental or corporate is an open question. But, contrary to thinking by folks like Mark Zuckerberg , those agencies should not also monitor the truth of news content. Zuckerberg saw the obvious difficulty in doing so : “I believe we must proceed very carefully though. Identifying the ‘truth’ is complicated.” This is correct, but misses the point. In order to combat fake news, Facebook does not need to become the “arbiter of truth.” Fake news is fake because of its intent, not content. So in order to regulate fake news, we need to delete bot and sockpuppet accounts, not build algorithms that detect false information.

On this score Facebook could improve. In a recent SEC filing , Facebook estimates that up to 5% of its monthly active users are false accounts. That means that as many as 140 million monthly users are using Facebook with deliberately deceptive intent. Moreover, these phony users have been given the ability to design custom bots that automate their communications with fellow Facebook users. Facebook is handing liars a megaphone. That may be good for business, but it is bad for our free speech culture.

Of course, there will be cases of alleged fake news – on Facebook or elsewhere – in which it is difficult to determine if there was an intent to deceive. But in this respect fake news does not differ from defamation. Both depend on determining the intention of the accused, and the burden of proof (for defamation: clear and convincing evidence) is consequently high. When it comes to restricting speech, having such a high burden of proof is a very good thing. It has prevented defamation case law from sliding down a slippery slope, and we should expect the same to hold for fake news regulation. It is no accident, though, that the crackdown on fake news is now coming most aggressively through such cases. Smartmatic recently filed a defamation lawsuit against Fox Corporation, seeking $2.7B in damages allegedly caused by fake news about its products.

This is not to say that there are no grounds for regulating false content. There may be cases where misinformation poses risks so great as to warrant its being removed or otherwise censored. Just as we should not be permitted to yell “fire!” in a crowded theater, there are things we should not be allowed to post on social media because they threaten the safety and integrity of the public sphere in which free speech is possible. But in this current media environment, where fake news is a primary source for such misinformation, to regulate content is to treat the symptom, not the disease. So while regulatory agencies like Facebook’s Oversight Board may deem it necessary to moderate content, their real focus should be on intent.

4.) Cancel Trump, not Parler

If all this is right, then Twitter was probably right to cancel Trump, but Amazon wrong to cancel Parler. According to the Washington Post, while in office President Trump made 30,573 false or misleading claims . The newspaper is reluctant to call any of them “lies,” but only because intent cannot be definitively determined. Nevertheless, a reasonable case can be made that President Trump eroded free speech culture, and that his bullhorn needed to be taken away, his social media accounts shut down, his press briefings no longer aired. For Parler, the case is different. Parler itself has not spread any fake news, although it provided a platform for those who do. Should we cancel Parler for that? Probably not – at least so long as we allow the lights to stay on at Facebook.

There are two lingering worries we might have about regulating fake news and those who produce it. Neither of these worries, though, gives us a compelling reason against regulation.

For one thing, we might fear that regulating fake news invites abuse. A regulating agency might misuse its power and restrict news deemed detrimental to its own interests. This seems to be the fear motivating German Chancellor Angela Merkel’s condemnation of Twitter’s decision to ban Trump. Abuses of regulatory power are no doubt possible, but their likelihood diminishes if we keep in mind that fake news is fake not on account of its false or partisan content, but rather on account of its deceptive intent. If the agency accordingly regulates only on the basis of intent, then it will be less likely to restrict news out of self-interest or greed.

We might also worry that regulation would have an overall chilling effect on free speech. But this, too, seems unlikely. The effect of punishing liars is to encourage people to express claims they genuinely believe, even if they turn out to be wrong. Similarly, the effect of punishing fake news would be to encourage people and organizations to share news they genuinely believe. We should expect, then, that regulating fake news is more apt to stimulate than to stymie the expression of sincere speech. And that would be truly welcome news.

• Carlo DaVia

Carlo DaVia  is a Lecturer in philosophy at Fordham University, as well as an instructor at the CUNY Latin/Greek Institute.This academic year he will also serve as a fellow at the UC Center for Free Speech and Civic Engagement.

• Donald Trump
• free speech
• Mark Zuckerberg
• philosophy of free speech
• Seana Shiffrin

RELATED ARTICLES

Why arguments (almost) never work: motivated reasoning and persuasion, the simpsons and ai(mmortality), barbie as philosophy, undrip’s limits on corrective reforms to the basic structure, coded displacement, lies, fiction, and the post office.

My anti-vax friends, religionists, corporatists, libertarians, and the most war-mongering militarists are – I’m convinced – sincere in their advocacy for policies that demonstrably produce unnecessary harm (evil).

Fox News classifies much of its content as “entertainment”. It’s intent (to the extent a legal fiction can be said to possess such a thing) is partisan politics, profit, and power.

What are the criteria by which we may sort intent in such situations?

LEAVE A REPLY Cancel reply

Save my name, email, and website in this browser for the next time I comment.

Notify me of follow-up comments by email.

Notify me of new posts by email.

WordPress Anti-Spam by WP-SpamShield

Currently you have JavaScript disabled. In order to post comments, please make sure JavaScript and Cookies are enabled, and reload the page. Click here for instructions on how to enable JavaScript in your browser.

Advanced search

Posts You May Enjoy

Forgiveness, obligation, and cultures of domination: a review of myisha cherry’s..., apa member interview: natalie martin, reflections on james agee and the good life, the union as a basic institution of society, beyond neutrality: rethinking scholarly engagement in global governance.

Fake news on Social Media: the Impact on Society

• Open access
• Published: 19 January 2022
• Volume 26 , pages 443–458, ( 2024 )

Cite this article

You have full access to this open access article

• Femi Olan   ORCID: orcid.org/0000-0002-7377-9882 1 ,
• Uchitha Jayawickrama 2 ,
• Emmanuel Ogiemwonyi Arakpogun 1 ,
• Jana Suklan 3 &
• Shaofeng Liu 4  

179k Accesses

35 Citations

37 Altmetric

Explore all metrics

Fake news (FN) on social media (SM) rose to prominence in 2016 during the United States of America presidential election, leading people to question science, true news (TN), and societal norms. FN is increasingly affecting societal values, changing opinions on critical issues and topics as well as redefining facts, truths, and beliefs. To understand the degree to which FN has changed society and the meaning of FN, this study proposes a novel conceptual framework derived from the literature on FN, SM, and societal acceptance theory. The conceptual framework is developed into a meta-framework that analyzes survey data from 356 respondents. This study explored fuzzy set-theoretic comparative analysis; the outcomes of this research suggest that societies are split on differentiating TN from FN. The results also show splits in societal values. Overall, this study provides a new perspective on how FN on SM is disintegrating societies and replacing TN with FN.

Similar content being viewed by others

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

Esma Aïmeur, Sabrine Amri & Gilles Brassard

Advances in Social Media Research: Past, Present and Future

Kawaljeet Kaur Kapoor, Kuttimani Tamilmani, … Sridhar Nerur

The disaster of misinformation: a review of research in social media

Sadiq Muhammed T & Saji K. Mathew

Avoid common mistakes on your manuscript.

1 Introduction

In cascading news and sensitive information, the fundamental principles are embedded in the concepts of truth as well as the theories of accuracy in communication (Brennen, 2017 ; Dwivedi et al., 2018 ; Orso et al., 2020 ; Pennycook et al., 2020 ). However, in the past five years or so, social media (SM) has redefined the structure, dimensions, and complexity of the news (Berkowitz & Schwartz, 2016 ; Copeland, 2007 ; Kim & Lyon, 2014 ). The impact of SM, specifically on political affairs, has been attracting more interest, as SM platforms, notably Twitter, Facebook, and Instagram, enable the broad sharing of information and news (Vosoughi et al., 2018 ). In addition to providing information, another main purpose of SM is to enable people to engage in social interaction, communication, and entertainment (Hwang et al., 2011 ; Kuem et al., 2017 ). In particular, many SM posts are looking for support, where reposting aims to spread messages via the multiplicative effect. Consequently, this study purpose is to address the research problem and gap which suggest that SM platform providers are doing little in tackling the spread and cascading of FN on SM.

By providing unlimited access to a large amount of information, people can share different beliefs and values (George et al., 2018 ; Kim et al., 2019 ; Rubin, 2019 ). However, the risks and implications of this new resource remain unclear to most of the population. One such risk is fake news (FN). FN, although unvetted, has a credible and professional appearance, ensuring that people cannot always distinguish it from true news (TN) (Kumar et al., 2018 ). The effects of FN cut across the society, for example, the spread of FN on SM determines how governments, organizations, and people respond to events in the society. Majority of FN is targeted to a specific sample of the population with the aim of promoting a certain ideology by stimulating strong beliefs and polarizing society (Chen & Sharma, 2015 ). According to Kumar et al. ( 2018 ); Lundmark et al. ( 2017 ); Tandoc et al. ( 2019 ), a periodic review of FN on SM is thus required to limit discord and violence by groups or individuals in society.

FN has become a major part of SM, raising doubts about information credibility, quality, and verification. Studies investigating the influence of FN on SM have appeared in various fields such as digital media, journalism, and politics; however, in-depth analyses of the impact of FN on society remain scarce. Furthermore, despite the growing body of research on FN and SM —a significant factor in the fight against FN —(Tandoc et al., 2018 ), an adequate review of the impact of FN in SM on society is also lacking.

Hence, The aim of this study is to explore the role of SM platform providers in reducing the spread of FN in the society, as the research gap identified from previous studies (Kim & Dennis, 2019 ; Kim et al., 2019 ; Knight & Tsoukas, 2019 ; Roozenbeek & van der Linden, 2019 ) on the limited research on the impact of FN on the society, leading to this study finding answers to the following research questions (RQs):

RQ1. Why is FN cascading impacting negatively on the society?

RQ2. Are the big SM organizations taking actions in reducing FN cascading?

Based on the foregoing, this study provides a holistic view of the three focus areas (FN, SM, and societal acceptance) by reviewing research publications, case studies, and experts’ opinions to produce a conceptual framework, an insightful and comprehensive meta-framework. This study then analyzes the associations among the three distinct fields from theoretical and practical perspectives. These associations derived from the literature are tested using an analytic technique called fuzzy set analysis to show if they are supported, thereby indicating society’s efforts to combat FN. We find that people’s interpretations of what is TN or FN affect societal efforts to reduce the spread of FN.

The findings of this study contribute to research on FN on SM, specifically looking at societal impacts. They provide experts and researchers in these fields with insights into how communities are effectively combating the spread of FN and how to implement the useful ideas from this research to strengthen the inputs in tackling FN on SM. Further, the findings of this research not only provide support for the associations but demonstrate a model for societal strategies to manage the spread of FN as well as fact-checking and information verification, thus equipping society with the tools to recognize the differences between FN and TN.

The remaining sections in this study are organized as follows: the theoretical development of the conceptual meta-framework explains the literature for the concept of FN, SM, and societal acceptance. This is followed by researched method section that describes the data, analysis and presents the results of the study. Further, there is a discussion section on the results, implications of this study for research, practice, and the society, finally limitations and future research.

2 Theoretical Development of the Conceptual Meta-Framework

FN is shaped to replicate TN by mimicking its characteristics (i.e. accuracy, verifiability, brevity, balance, and truthfulness) to mislead the public (Han et al., 2017 ; Kim & Dennis, 2019 ; Kim et al., 2019 ). FN is not a new phenomenon, according to Burkhardt ( 2017 ), FN can be traced back to at least Roman times when the first Roman Emperor had to announce fake news to encourage Octavian to destroy the republican system. During the Roman period, there was no way of verifying and validating the authenticity of news, as challenging authority was classed as treason. The 20th century heralded a new era of numerous one-to-many communication modes such as newspapers, radio stations, and television stations, marking the beginning of misinformation in news (Aggarwal et al., 2012 ; Kim & Dennis, 2019 ; Kim et al., 2019 ; Knight & Tsoukas, 2019 ; Manski, 1993 ; Preti & Miotto, 2011 ; Roozenbeek & van der Linden, 2019 ). With the emergence of multimedia corporations, the content of FN has been gaining new audiences (Oestreicher-Singer & Zalmanson, 2013 ), and the arrival of the Internet towards the end of the century improved the phenomenon of FN (Kapoor et al., 2018 ). As technology advanced in the 21st century, SM arrived, multiplying the dissemination of FN using both one-to-many and many-to-many strategies.

2.1 Understanding FN

FN content, which is divided into individual opinions and scientific consensus on trending issues such as COVID-19, evolution, and climate change, has long existed (Knight & Tsoukas, 2019 ). However, constant changes in political strategies have fundamentally impacted how information is defined, viewed, and interpreted at all levels of communication (Massari, 2010 ). Aggarwal and colleagues argued that incorrect scientific, political, and belief-oriented information has significant causes and consequences on individuals that are more politically inclined and those aiming to drive their ideas to wider society (Aggarwal et al., 2012 ). Therefore, individuals actively seeking information are united in their pursuit of knowledge and political action (Aggarwal & Singh, 2013 ). It is impossible to change their values and beliefs, abandon old ways and accept the fact-checked news, new methods to enlightening individuals or people with similar beliefs to adopt new states to a degree of news verification and validation (Cao et al., 2015 ; Centeno et al., 2015 ; Kim & Lyon, 2014 ).

As FN is fundamentally built on untraced and misleading phenomena, experts and researchers have noted a rising interest in the development of fact-checking tools to spot the spread of FN content in society (Berkowitz & Schwartz, 2016 ; Hwang et al., 2011 ; Miranda et al., 2015 ; Miranda et al., 2016 ). However, despite the large investment in innovative tools for identifying, distinguishing, and reducing factual discrepancies (e.g., ‘Content Authentication’ by Adobe for spotting alterations to original content), the challenges concerning the spread of FN remain unresolved, as society continues to engage with, debate, and promote such content (Kwon et al., 2017 ; Pierri et al., 2020 ). Indeed, the gap between fact-checking and the fundamental values and beliefs of the public discourages people from promoting fact-checking rather than accepting the dangers of FN (Kim & Lyon, 2014 ; Lukyanenko et al., 2014 ). Therefore, these tools do little to reduce the spread of FN in practice.

2.2 SM and Society

SM provides an environment in which individuals can exchange personal, group, or popular interests to build relationships with people that have similar and/or diverging beliefs and values. For example, most people of a particular age group share similar interests courtesy of growing up in the same era (Gomez-Miranda et al., 2015 ; Lyon & Montgomery, 2015 ; Miller & Tucker, 2013 ; Nerur et al., 2008 ). People’s characteristics are often inherited from educational institutions, communities, and family lifestyles (Matook et al., 2015 ). Further, certain age groups continue to hold onto specific values and beliefs, as reflected in the public’s response to the 2016 and 2020 U.S. presidential election and the 2019 UK general election (Prosser et al., 2020 ; Wang et al., 2016 ). Accordingly, Venkatraman et al. ( 2018 ) argued that values and beliefs are passed down through family generations, making it possible for a group in society to continue to hold onto specific philosophies.

SM plays an important role in helping people reconnect with friends and families as well as find jobs and purchase products and services (Kim & Dennis, 2019 ; Leong et al., 2015 ; Lyon & Montgomery, 2015 ; Miller & Tucker, 2013 ; Nerur et al., 2008 ; Pierri et al., 2020 ). SM platforms are also channels for recruiting interested parties for the continuity and propagation of a long-held ideology. Moreover, people with common demographic attributes use the instant messaging services on SM to communicate more than those without such shared demographics (Baur, 2017 ). SM platforms are thus online services that mirror real-world activities (e.g., dating services from Facebook, live Instagram feeds from parties).

The societal acceptance strategy can reduce the spread of FN (Haigh et al., 2018 ; Lundmark et al., 2017 ; Lyon & Montgomery, 2015 ; Miller & Tucker, 2013 ; Nerur et al., 2008 ; Sommariva et al., 2018 ). However, the expansion of multiple access points for information and news sharing on SM platforms contributes more to the spread of falsity than reducing its impact. Nevertheless, societal acceptance is considered to be a game-changer for controlling the spread of FN by SM (Egelhofer & Lecheler, 2019 ). Some empirical studies have analyzed the spread and flow of FN online (Garg et al., 2011 ; Gray et al., 2011 ), but little research examines how human judgment can differentiate truth from falsity. To reduce the spread of FN in society, it is important to understand the triangle of FN, the relationships between the constructs from each circle, and the associations that bind the circles, and then analyze the strength of the relationship (Chang et al., 2015 ; Chen & Sharma, 2015 ; Matook et al., 2015 ).

2.3 Meta-framework on the Impact of FN

This study developed a meta-framework based on the literature on FN, SM, and societal acceptance. Each of these perspectives, depicted as circles in the meta-framework, discusses the constructs that contribute to defining the clusters in theory. The constructs that then emerge from each perspective are the foundation for the meta-framework discussing the relationships among their associations. This study further develops notations to define the associations. By combining the three defined circles, these perspectives provide a new theoretical framework, as previous studies have shown that feasibilities to conceptualize phenomenon are at a wide spectrum (Table  1 ).

This study adopted the epidemiological model as a suitable theory for discussing the meta-framework perspectives. In particular, it employed the conceptual model of the disease triangle. In the 1960 s, the disease triangle was developed by George McNew to understand the pathology and epidemiology of plants and their diseases (Scholthof, 2007 ). This model stated that for a disease to manifest, three fundamental elements are required: the environment; the infectious pathogen that carries the virus, bacteria, or other micro-organisms; and the host. In this study, FN is defined as an ‘infectious pathogen’, as it is an epidemic that consists of varieties of fake news (Pan et al., 2017 ). According to Scholthof ( 2007 ), the environment determines whether the infection can be controlled; here, as shown in Fig.  1 , SM is conceptualized as the environment, the hosts are the readers, individuals, and society.

Fake news triangle

SM as an environment for cascading of FN has a structure (Chen et al., 2015 ; Miller & Tucker, 2013 ; Scholthof, 2007 ). The aim of the SM structure is to generate contents that attract millions of views by re-sharing news or information targeting a set of specific viewers. As the contents are shared and attained a viral status in the society, SM organizations are leveraging increased profits (Mettler & Winter, 2016 ). Primarily, SM structure is designed on contents ranking system constructed by algorithm ranking techniques, the method of data management and significance leveling in data priority (Hamamreh & Awad, 2017 ). News and information are ranked in a methodological order that links constructing a natural distribution by connecting between nodes of the SM (Gerlach et al., 2015 ; Matook et al., 2015 ). To understand the ranking system in SM, each node is assigned a unique code by creating iterative process of weights in network, these weights are assigned according to the content structure of the SM node (Brennen, 2017 ; Burkhardt, 2017 ; Chen, 2018 ). According to Brennen ( 2017 ); Burkhardt ( 2017 ); Chang et al. ( 2014 ); Chen ( 2018 ); Maier et al. ( 2015 ); Massari ( 2010 ), SM as the environment for infectious contents like FN comprises of communication channels such as websites, mobile applications, and platforms that facititate relationship forming among users of contents with similar interest. Hence, the relevance of SM to various aspects of life is of high singficance to users, government policies, and the economy.

This is somewhat consistent with the argument of the Director-General of the World Health Organization (WHO) – Tedros Ghebreyesus – at a foreign policy and security expert submit held in Germany in February 2020 (Union, 2020 , May 19). Tedros argued that as the world continues to grapple with Covid-19 contagion, an ‘infodemic’ is emerging as FN continues to “spread faster and more” than Covid-19 (Africe, 2020 ). Given the speed of the spread of FN, infodemic can hinder the effectiveness of public health response while propagating confusion and distrust in the society.

As shown in Fig.  1 , the hosts interact with those who have similar interests in their SM groups or forums and thus recruit new believers to the environment (Haigh et al., 2018 ; Humprecht, 2019a ; Mettler & Winter, 2016 ; Roozenbeek & van der Linden, 2019 ; Rubin, 2019 ). These communities continue to grow as positive social networks expand. With the power of SM platforms, new groups are created that have a similar agenda, improving social learning and opportunities using SM platforms’ tools (Kwon et al., 2017 ). One of the purposes of these strategies and networks is to clamp down as quickly as possible on people perceived as outsiders that may uncover or expose their content and philosophies.

3 Research Method

3.1 research design and data collection.

This study carried out a longitudinal survey with online participants to test the relationships and associations in the proposed meta-framework. A cross-sectional online survey was conducted in 2019, survey was conducted using stratified sampling, with participants divided into groups based on their demographics, proficiency of using SM platforms, and interest in news and current affairs online. Table  2 shows participants’ profiles in terms of their gender, age, location, SM usage, and SM experience. The questionnaire was designed through the research gap and literature.

This study distributed the questionnaire to 2234 active engaging participants and received 546 surveys which included both partial and completed questionnaire, which accounts for a response rate of 24%, demonstrating that the response rate is consistent with previous studies (Arshad et al., 2014 ; Klashanov, 2018 ; Malik et al., 2020 ). This study sample size consists of participants from across the global, with North America accounting for 29% of the total survey which make up for the largest share in terms of participant size. Experience of using SM platforms show that 28% of the participants engage more than 5 times daily on the platforms while 22.7% accounting for participants with 5 to 6 years working the SM platforms.

3.2 Analytical Technique

According to Ragin ( 2013 ); Ragin and Pennings ( 2005 ), the fuzzy set theoretical approach can be used to evaluate theories, frameworks, and models with a deductive strategy driven by a positivist paradigm. Fuzzy set analysis is an emerging technique for management and social sciences, which has become more popular as the initial problems were overcome by introducing hybrid techniques of fuzzy set logic. This study adopts the relationship and association testing suggested by Ragin ( 2009 ) to test for Boolean expressions in the fuzzy set theoretical approach of the four intersections in Fig.  2 .

Integrated meta-framework

This study proposes an eight-step process flowchart consisting of four loop relationships (represented by the double line diamonds in Fig.  3 ) and three predictive relationships (represented by the single line diamonds) that shows the relationships used to discuss the outcomes of the analysis. The flowchart is described as follows:

Flow chart for the consistency analysis

A loop relationship for an expression that a solution pathway is reliable shows whether the consistency of the sufficiency analysis is greater than 0.7 of the solution pathways as defined in this paper for the consistency threshold analysis. Any relationship that falls below the set threshold is eliminated from further analysis testing, as this means that that relationship does not achieve acceptable reliability.

A loop relationship for an expression that a solution pathway is accepted shows whether the consistency of A1 is greater than 0.7. This statement suggests that any relationship that falls below the acceptable criteria in the solution pathway must be rejected.

A double line diamond relationship for a strongly supported expression shows whether the consistency of A2, A3, and A4 is less than or equal to 0.7. This statement suggests that any relationship that passes the acceptance criteria does not have significant contradictory proofs.

A single line diamond relationship for an expression not supported by itself (however, subsequent relationships can benefit) can be described by the consistency of A3, which is less than or equal to 0.7. Furthermore, A3 represents the type I consistency error, and it is usually below the acceptance threshold.

A loop relationship for an expression that a solution pathway is weakly supported shows whether the consistency of the sufficiency analysis that A1 is greater than A3 of the solution pathways, as defined for the consistency threshold analysis. Any relationship that falls below the set threshold is eliminated from further analysis, as the relationship does not achieve acceptable reliability.

A double line diamond relationship for a supported expression shows whether the consistency of A4 is less than or equal to 0.7. This statement suggests that any relationship that passes the acceptance criteria does not have a significant error during analysis and this supports classification.

A loop relationship for an expression that a solution pathway is not weakly supported shows whether the consistency of A2 is greater than 0.7. This statement suggests that any relationship that falls below the acceptable criteria in the solution pathway can be improved and there is weak support for classification.

A double line diamond relationship for a supported expression shows whether the consistency of A2 is greater than or equal to A4. This statement suggests that any relationship that passes the acceptance criteria and partially supports the conditions for A2 and A4 represents the type II consistency error; this is usually equal to or greater than the acceptance threshold.

4 Data Analysis and Results

According to Deutsch and Malmborg ( 1985 ), complementarity and equifinality, the two underlying features in the fuzzy set theoretic approach, display patterns of attributes and different results depending on the structure of the constructs. In addition, the attributes in the constructs are concerned with the present or absent conditions and associations formed during conceptualization, rather than isolating the attributes from the constructs. Furthermore, complementarity exists if there is proof that causal factors display a match in their attributes and the analysis shows a higher level in the results, while equifinality exists if at least two unidentical pathways known as causal factors show the same results (Herrera-Restrepo et al., 2016 ).

In Table  3 , the attributes of the constructs indicate the relationships that provide empirical evidence to reject or support the model. The results demonstrate that the relationships are mostly rejected. We find that a higher consistency level directly results in a higher reliability of the relationship. The three combinations of attributes in the sufficiency analysis show that the input efficiency either fails or passes the set consistency threshold requirement (consistency and coverage are 0.72 and 0.44, respectively).

In Table  4 , the relationships indicate support for the empirical findings. The results show that the attributes of the constructs have higher combined solution pathways than the attributes in Table  3 . The type II error (or false negative) is one form of contradiction ignored in Fig.  3 . These findings show the least likely attributes of the constructs, indicating the continuation of existing relationships as well as supporting the higher consistency level of the associations and stronger support for further relationships. Hence, this analysis can introduce additional causal conditions of similar attributes not yet shown in the current relationships by retracking to the relationship mapping data and finding common attributes in existing constructs. This may explain the undefined variance in the existing relationships.

Table  5 shows the combined solution pathways for consistency and coverage, indicating support for most of the attributes of the constructs. This indicates a type I error (or false positive) in the form of contradicting the variances in the relationships, while the higher consistency level of the associations supports the higher values that delimit the relationships. Therefore, unconfirmed attributes indicate a restriction of the current relationships.

In Table  6 , this combined solution pathway indicates that neither the predicted relationships nor the coverage by attributes’ definitions of the constructs are strongly supported in terms of societal acceptance and the challenges posed by FN on SM on society. Therefore, alternative variances, as understood by the society, are better-supporting conditions for the relationship’s definitions in A4. Five of the six pathways are equal to or greater than the defined threshold, indicating that the relationships between the constructs can benefit from trade-offs. Furthermore, there are similar results for the unique coverage, signaling a significantly high-efficiency input directly linked to the variance from the causal conditions.

To fully understand the A4 outcomes, it is important to discuss the outcomes from A1, A2, and A3 simultaneously. A1 and A2 are insufficient to support a high input efficiency, indicating that SM will fade-out without a correlation with FN. To have a high input efficiency, the combination of the two constructs is highly significant to the relationships. However, A3, which considers all the attributes in the societal acceptance constructs, rejects the associated attributes from A1, whereas it shows weak support for A2, which indicates that the conditions are peripheral or are unconcerned about the variance. This explains the weak support in the attributes of their relationships. The A4 outcome shows that this study considers the attributes of the relations between A1 and A2, as A3 can explain the outcomes of redefining and reducing the impact of both associations.

5 Discussion

The aim of this research was to carry out an investigation on the impact of FN on the society, the use of SM as a platform for cascading of information and news. Thus, this study further explore the conceptual model of disease triangle (Piccialli et al., 2021 ) which identify FN as infectious pathogen in Fig.  1 (SM platforms host and spread FN), without the societal acceptance, it is difficult to cascade information and news. Furthermore, FN as defined in this study holds three main features which are significant for the perceptions of the society: the contents of the news, the intentions of the news, and the verification of the news. Hence, the use of comparative technique (fsQCA analysis) to outline the findings as shown in this study auggesting that societal acceptance is important in understanding the impact of FN. To better understand FN, SM, and societal acceptance, this study developed a meta-framework and analyzed the relationships among the attributes of the three constructs within. An online survey with 356 participants was carried out with a stratified sample size to test the meta-framework, and the data collected from the survey process were further categorized as the relationships designed in the constructs. This study considered SM platforms and the activities stimuling cascading processes of FN, changing the societal acceptance through the lens of contents management.

In previous studies, SM platforms are increasingly changing business activities and strategies used in positioning new products and brands, also leading to mis-information in the society (Modgil et al., 2021 ; Parra et al., 2021 ; Piccialli et al., 2021 ), also analyzed the SM platforms as the environment for business and social transactions focusing on capturing the largest audiences for information cascading, this further the spread of FN through the use of cascading tools available on SM. According to (Dwivedi et al., 2018 ; Kim & Dennis, 2019 ; Kim et al., 2019 ), cascading of FN through the use of SM platforms is growing faster than anticipated. The results of this study identified focused areas that can reduce the spread of FN on SM.

The results gathered during data analysis of validated questionnaire demonstrated important contributions of this study to minimizing cascading of FN in the society. Thus, the evaluation of the three perspectives; FN, SM, and societal acceptance further enhanced into relationship mapping by considering the entities from each perspectives as shown in Fig.  2 . The results from Table  3 , suggest that the testing of the relationship A1: FN/USˑVA of FN perspective and the entities users and values of the societal perspective is rejected while the relationship A1: FN/USˑNW of FN perspective and the entities users and networks of the societal acceptance is supported. Furthermore, the outcomes in Table  3 concur with the disease triangle theory which discussed the pathology model for disease manifestation, stating that the three triangular elements for infectious pathogen must be present for disease to grow (Humprecht, 2019b ; Rubin, 2019 ; Sommariva et al., 2018 ). Hence, the relationship A1: FN/USˑVA of FN perspective and the entities users and values of the societal perspective lacks the environment (networks) for cascading of contents of FN.

Table  4 shows support for SM and societal acceptance perspectives relationship mapping, with constructs’ consistency and coverage meeting the set requirement in Fig.  3 . However, condition S1 and S2 for A2: SM/USˑVA and S1 for A2: SM/USˑNW were ignored from the result, suggesting that there are other sources of information such as true news, entertainment contents which users are engaging with on SM platforms. According to Kwon et al. ( 2017 ), SM platforms provide positive opportunities such as learning new skills, engaging with experienced individuals and mentors, and finding new friendship, directly impacting positively on the society.

The increase in the level of cascading of FN can be attributed to SM companies drive to upsurge the size of big data, leading to strategic end to end nodes multiplication (Haigh et al., 2018 ). This study demonstrates that the enabling environment for the spreading of FN is attributed to the structure and strategies of SM companies. As shown in Table  6 , when SM companies implement effective fact-checking tools on SM platforms, the traffic of FN is minimized and the impact on the society is reduced. The relevant role of SM companies is to ensure that verification and fact-checking are embedded into the process of retrieving news and information.

In summary, the findings of this study suggest that previous studies (Dwivedi et al., 2018 ; Kim et al., 2019 ; Malik et al., 2020 ; Modgil et al., 2021 ; Roozenbeek & van der Linden, 2019 ) demonstrated the gap for an investigation of the societal acceptance of contents available on SM. Our findings show that the societal acceptance of information and news is highly dependent on the verification and fact-checking features that are available on the SM platforms. Therefore, the research questions in this study outlined the need for fact-checking and verification of information and news most importantly FN on SM. The results of the complementarity assessments show that SM and societal acceptance did significantly influence cascading of contents towards users. Specifically, FN cascading spread faster than any other type of contents on SM as shown in Table  5 . With regards to societal acceptance, users distributions of FN contents unconsciously aid cascasding with the intention of spreading awareness about the situation surrounding FN events.

5.1 Theoretical Implications

This study builds on the theoretical knowledge in literature by making significant contribution to the understanding of the impact of FN and SM platforms on the society. According to studies (Abouzeid et al., 2021 ; Au et al., 2021 ; Dwivedi et al., 2018 ; Kim et al., 2019 ; Parra et al., 2021 ; Tran et al., 2021 ) with combined body of knowledge on misinformation, FN, SM, SM platforms, cascading of FN, and risks of misinformation, this study identifies three main themes in our contribution: FN, SM, and societal acceptance. Previous studies (Orso et al., 2020 ; Pennycook et al., 2020 ) have presented FN and SM concepts, however this study’s introduction of societal acceptance is a novel theoretical contribution. Furthermore, the lack of studies on the societal acceptance of cascading of FN have generated a theoretical gap in understanding FN, misinformation and SM. Therefore, the results in our paper filled the research gap by validating the proposed features of societal acceptance: users, networks, and values.

The findings of this study contribute to theory by using complementarity among FN, SM, and societal acceptance to explain their influence by evaluating all the attributes in the three constructs, building relationships, and presenting findings that identify the significance of each association to reduce the cascading of FN in society. Therefore, this research answers the call of studies (George et al., 2018 ; Miller & Tucker, 2013 ; Miranda et al., 2016 ) that have suggested further work on FN on SM. Further, this study explains the impact of FN on society by exploring the conditions in different scenarios and with different complementarity values. It also shows how SM (i.e., the environment) and users can strategically deploy all resources to tackle the cascading and spread of FN. Most importantly, fuzzy set theory provides a data analysis structure that shows complex causality, enabling this research to present empirical findings.

Theoretically speaking, the outcomes show the importance of fact-checking and managing cascading in reducing the spread of the contents of FN in the society. Also, the role of SM companies in continuance commitment to support the course of minizing the impact of FN. As of date, this is the first of study to develop a meta-framework to examine the impact of FN on the society distributed on the SM. This study argued that exploring fact-checking and managing cascading will provide a platform for SM companies to contributing in the challenging impact of FN on the society. This study finds that SM as a type of environment is equipped with the technological know-how to tackle the spread of FN. This is particularly so for large SM organizations such as Facebook whose main business is SM content. Therefore, investment in technological research and service innovation is becoming a priority. However, more investment is required for fact-checking and analyzing cascading news, meaning that SM organizations with technical research facilities are more likely to initiate rigorous fact-checking campaigns. Hence, profitability and market growth may be more important for implementing fact-checking and news-cascading technologies that benefit society.

5.2 Practical Implications

Based on the outcomes obtained from the complementarity of the fuzzy set, it is also important for the SM platform providers to continue to invest in the fact-checking and managing contents of FN that are influencing users perceptions. In addition, it is very important to manage the direct impact of FN contents on the society by increasing the amount of fact-checking and verification tools that are available on SM. For instance, vigorous campaigns on the important role of news and information verification across all SM platforms and ensuring that there is educating information about the impact of spreading FN on SM on the society at large. Also, SM organizations should implement safe technology such as real-time deletion of contents of FN to ensure a safer communication environment for the users. Furthermore, the distinguishing real news from fake news using aided technology will boost confidence in the society. The comprehensive theoretical review and in-depth empirical analysis of the complex casualty of FN on SM on society in this study allows SM organizations to consider their organizational strategies to reduce FN cascading and implement sustainable solutions. SM organizations should prioritize the allocation of resources toward measures that tackle the challenges FN poses to society as well as the cost, societal impact, and misinformation linked to regulations to halt the spread of FN.

5.3 Implications for Society

The in-depth empirical analysis conducted concerning the FN on SM and the societal impact, the study provides a platform to the SM users on how far the facts published on SM can be trusted and how to filter the FN from TN on SM. SM organizations such as Facebook and Twitter have invested in large to tackle the publishing of FN on social media while yet the FN has taken on SM drastically during certain urgent situations.

Following the countless challenges that arose around the world due to the FN published on SM and the societal impact, the SM organizations have taken larger steps in minimizing the FN before being published and open to the public. The flowchart for the consistency analysis can be used by SM organizations in analyzing the published news on SM to distinguish FN from TN. Thus, the negative impact caused by FN to users and their lives can be minimized. Despite the fact that steps been taken by the SM organizations, it is also users’ responsibility to filter TN from FN even if they are being posted on verified accounts, by fact-checking or using appropriate verification (Nagi, 2020 ).

6 Conclusions

The results from this study demonstrate that it is important for SM platform providers continue in their efforts to understand the risks of cascading of FN and the influence on the society at large. Hence, the implementation of fact-checking tools is significant in reducing the spread of FN, building of trust and confident in the society. SM platform providers should ensure that there is continuous monitoring of online activities triggered by spread of FN and also ensures periodic upgrade of fact-checking technologies to tackle new tricks and strategies used in cascading FN in the society (Modgil et al., 2021 ; Parra et al., 2021 ). Furthermore, fact-checking information and public awareness on how to verify news can be added to campaigns to support the affected societies in combating the impact of FN. The findings in our study demonstrate that societal acceptance is a powerful tool that can persuade the society to focus on achieving common goal. The role of the society is to adopt the strength in societal acceptance to drive positive cultural change that welcome fact-checking and verification of any form of news.

6.1 Limitations and Future Research Directions

This study, like other studies, has limitations that suggest future research directions. This study analyzed how three constructs, FN, SM, and societal acceptance, impact on society. Other constructs were not included in this study such as SM firms’ power, political strategies, and societal perceptions. In addition, our data collection focused on people who engage most frequently with SM; experts and SM analysts may be relevant for future research to examine. Given that previous researchers focus on cascading FN and fact-checking news content to distinguish TN from FN, the influence of fact-checking and analyzing FN cascading could be tested future research with new datasets. In this vein, this study did not consider the financial impact of FN on SM on society, which is another interesting area for future research.

This cross-sectional research aimed to provide an in-depth understanding of the relationships of the three studied topics by analyzing data from many demographics rather than from one location. Therefore, the findings of this study support generalization to many locations. However, since some studies consider the results from a single location, future research could compare the complementarity, consistency, and coverage of a single location with many locations, which would enrich the findings of this study.

Abouzeid, A., Granmo, O. C., Webersik, C., & Goodwin, M. (2021). Learning automata-based misinformation mitigation via Hawkes processes. Information Systems Frontiers, 23 (5), 1169–1188. https://doi.org/10.1007/s10796-020-10102-8 .

Article   Google Scholar  

Africe, W. R. O. (2020). f. Technical Guidance on contact tracingor COVID-19 in the World Health Organization (WHO) African region .  https://www.afro.who.int/publications/technical-guidance-contact-tracing-covid-19-world-health-organization-who-african . Accessed 19 May 2020.

Aggarwal, R., Gopal, R., Sankaranarayanan, R., & Singh, P. V. (2012). Blog, blogger, and the firm: can negative employee posts lead to positive outcomes? Information Systems Research, 23 (2), 306–322. https://doi.org/10.1287/isre.1110.0360 .

Aggarwal, R., & Singh, H. (2013). Differential influence of blogs across different stages of decision making: the case of venture capitalists.(Report). Mis Quarterly, 37 (4), 1093. https://doi.org/10.25300/MISQ/2013/37.4.05 .

Arshad, M., Islam, S., & Khaliq, A. (2014). Fuzzy logic approach in power transformers management and decision making. IEEE Transactions on Dielectrics and Electrical Insulation, 21 (5), 2343–2354. https://doi.org/10.1109/TDEI.2014.003859 .

Au, C. H., Ho, K. K. W., & Chiu, D. K. W. (2021). The role of online misinformation and fake news in ideological polarization: barriers, catalysts, and implications. Information Systems Frontiers . https://doi.org/10.1007/s10796-021-10133-9 .

Barrett, M., Oborn, E., & Orlikowski, W. (2016). Creating value in online communities: the sociomaterial configuring of strategy, platform, and stakeholder engagement. Information Systems Research, 27 (4), 704–723. https://doi.org/10.1287/isre.2016.0648 .

Baur, A. (2017). Harnessing the social web to enhance insights into people’s opinions in business, government and public administration. Information Systems Frontiers, 19 (2), 231–251. https://doi.org/10.1007/s10796-016-9681-7 .

Berkowitz, D., & Schwartz, D. A. (2016). Miley, CNN and The Onion. Journalism Practice, 10 (1), 1–17. https://doi.org/10.1080/17512786.2015.1006933 .

Brennen, B. (2017). Making sense of lies, deceptive propaganda, and fake news. Journal of Media Ethics, 32 (3), 179–181. https://doi.org/10.1080/23736992.2017.1331023 .

Brummette, J., Distaso, M., Vafeiadis, M., & Messner, M. (2018). Read all about it: the politicization of “Fake News” on Twitter. Journalism & Mass Communication Quarterly, 95 (2), 497–517. https://doi.org/10.1177/1077699018769906 .

Burkhardt, J. M. (2017). History of fake news. Library Technology Reports, 53 (8), 5–9.

Google Scholar  

Cao, X., Guo, X., Liu, H., & Gu, J. (2015). The role of social media in supporting knowledge integration: A social capital analysis. Information Systems Frontiers, 17 (2), 351–362. https://doi.org/10.1007/s10796-013-9473-2 .

Centeno, R., Hermoso, R., & Fasli, M. (2015). On the inaccuracy of numerical ratings: dealing with biased opinions in social networks. Information Systems Frontiers, 17 (4), 809–825. https://doi.org/10.1007/s10796-014-9526-1 .

Chang, I. C., Liu, C. C., & Chen, K. (2014). The push, pull and mooring effects in virtual migration for social networking sites. Information Systems Journal, 24 (4), 323–346. https://doi.org/10.1111/isj.12030 .

Chang, W. L., Diaz, A., & Hung, P. (2015). Estimating trust value: A social network perspective. Information Systems Frontiers, 17 (6), 1381–1400. https://doi.org/10.1007/s10796-014-9519-0 .

Chen, H., De, P., & Hu, Y. J. (2015). IT-enabled broadcasting in social media: an empirical study of artists’ activities and music sales. Information Systems Research, 26 (3), 513–531. https://doi.org/10.1287/isre.2015.0582 .

Chen, R., & Sharma, S. K. (2015). Learning and self-disclosure behavior on social networking sites: the case of Facebook users. European Journal of Information Systems, 24 (1), 93–106. https://doi.org/10.1057/ejis.2013.31 .

Chen, X. (2018). Calling out fake news on social media: a comparison of literature in librarianship and journalism. Internet Reference Services Quarterly, 23 (1–2), 1–13. https://doi.org/10.1080/10875301.2018.1518284 .

Copeland, D. A. (2007). A series of fortunate events: why people believed Richard Adams Locke’s “Moon Hoax.” Journalism History, 33 (3), 140–150.

Deutsch, S. J., & Malmborg, C. J. (1985). Evaluating organizational performance-measures using fuzzy subsets. European Journal of Operational Research, 22 (2), 234–242. https://doi.org/10.1016/0377-2217(85)90231-0 .

Dwivedi, Y. K., Kelly, G., Janssen, M., Rana, N. P., Slade, E. L., & Clement, M. (2018). Social media: the good, the bad, and the ugly. Information Systems Frontiers, 20 (3), 419–423. https://doi.org/10.1007/s10796-018-9848-5 .

Egelhofer, J. L., & Lecheler, S. (2019). Fake news as a two-dimensional phenomenon: a framework and research agenda. Annals of the International Communication Association, 43 (2), 97–116. https://doi.org/10.1080/23808985.2019.1602782 .

Fang, X., Hu, P. J. H., Li, Z., & Tsai, W. (2013). Predicting adoption probabilities in social networks. Information Systems Research, 24 (1), 128–145. https://doi.org/10.1287/isre.1120.0461 .

Garg, R., Smith, M. D., & Telang, R. (2011). Measuring information diffusion in an online community. Journal of Management Information Systems, 28 (2), 11–38.

George, J. F., Gupta, M., Giordano, G., Mills, A. M., Tennant, V. M., & Lewis, C. C. (2018). The effects of communication media and culture on deception detection accuracy. MIS Quarterly: Management Information Systems, 42 (2), 551–575. https://doi.org/10.25300/MISQ/2018/13215 .

Gerlach, J., Widjaja, T., & Buxmann, P. (2015). Handle with care: How online social network providers’ privacy policies impact users’ information sharing behavior. Journal of Strategic Information Systems, 24 (1), 33–43. https://doi.org/10.1016/j.jsis.2014.09.001 .

Gomez-Miranda, M. E., Perez-Lopez, M. C., Argente-Linares, E., & Rodriguez-Ariza, L. (2015). The impact of organizational culture on competitiveness, effectiveness and efficiency in Spanish-Moroccan international joint ventures. Personnel Review, 44 (3), 364–387. https://doi.org/10.1108/Pr-07-2013-0119 .

Gray, P., Parise, S., & Iyer, B. (2011). Innovation impacts of using social bookmarking systems. Mis Quarterly, 35 (3), 629–643. https://doi.org/10.2307/23042800 .

Haigh, M., Haigh, T., & Kozak, N. I. (2018). Stopping fake news. Journalism Studies, 19 (14), 2062–2087. https://doi.org/10.1080/1461670X.2017.1316681 .

Hamamreh, R. A., & Awad, S. (2017). 14-16 Dec. 2017). Tag ranking multi-agent semantic social networks. 2017 International Conference on Computational Science and Computational Intelligence (CSCI)

Han, J., Lee, S. H., & Kim, J. K. (2017). A process integrated engineering knowledge acquisition and management model for a project based manufacturing (Vol 18, pg 175, 2017). International Journal of Precision Engineering and Manufacturing , 18 (3), 467-467. https://doi.org/10.1007/s12541-017-0056-x

Herrera-Restrepo, O., Triantis, K., Trainor, J., Murray-Tuite, P., & Edara, P. (2016). A multi-perspective dynamic network performance efficiency measurement of an evacuation: A dynamic network-DEA approach. Omega-International Journal of Management Science, 60, 45–59. https://doi.org/10.1016/j.omega.2015.04.019 .

Humprecht, E. (2019). How do they debunk “fake news”? A cross-national comparison of transparency in fact checks. Digital Journalism . https://doi.org/10.1080/21670811.2019.1691031 .

Humprecht, E. (2019). Where ‘fake news’ flourishes: a comparison across four Western democracies. Information Communication and Society, 22 (13), 1973–1988. https://doi.org/10.1080/1369118X.2018.1474241 .

Hwang, Y. C., Yuan, S. T., & Weng, J. H. (2011). A study of the impacts of positive/negative feedback on collective wisdom—case study on social bookmarking sites. Information Systems Frontiers, 13 (2), 265–279. https://doi.org/10.1007/s10796-009-9186-8 .

Kapoor, K., Tamilmani, K., Rana, N., Patil, P., Dwivedi, Y., & Nerur, S. (2018). Advances in social media research: past, present and future. Information Systems Frontiers, 20 (3), 531–558. https://doi.org/10.1007/s10796-017-9810-y .

Kim, A., & Dennis, A. R. (2019). Says who? The effects of presentation format and source rating on fake news in social media. MIS Quarterly: Management Information Systems, 43 (3), 1025–1039. https://doi.org/10.25300/MISQ/2019/15188 .

Kim, A., Moravec, P. L., & Dennis, A. R. (2019). Combating fake news on social media with source ratings: the effects of user and expert reputation ratings. Journal of Management Information Systems, 36 (3), 931–968.

Kim, E. H., & Lyon, T. (2014). Greenwash vs. Brownwash: Exaggeration and undue modesty in corporate sustainability disclosure. Organization Science, 26 (3), 705–723. https://doi.org/10.1287/orsc.2014.0949 .

Klashanov, F. (2018). Fuzzy logic in construction management. MATEC Web of Conferences , 170 . https://doi.org/10.1051/matecconf/201817001111

Knight, E., & Tsoukas, H. (2019). When Fiction Trumps Truth: What ‘post-truth’ and ‘alternative facts’ mean for management studies. Organization Studies, 40 (2), 183–197. https://doi.org/10.1177/0170840618814557 .

Kuem, J., Ray, S., Siponen, M., & Kim, S. S. (2017). What leads to prosocial behaviors on social networking services: a tripartite model. Journal of Management Information Systems, 34 (1), 40–70. https://doi.org/10.1080/07421222.2017.1296744 .

Kumar, N., Venugopal, D., Qiu, L., & Kumar, S. (2018). Detecting review manipulation on online platforms with hierarchical supervised learning. Journal of Management Information Systems, 35 (1), 350–380.

Kwon, H. E., Oh, W., & Kim, T. (2017). Platform structures, homing preferences, and homophilous propensities in online social networks. Journal of Management Information Systems, 34 (3), 768–802.

Lazer, D. M. J., Baum, M. A., Benkler, Y., Berinsky, A. J., Greenhill, K. M., Menczer, F. … Zittrain, J. L. (2018). The science of fake news: Addressing fake news requires a multidisciplinary effort. Science , 359 (6380), 1094–1096. https://doi.org/10.1126/science.aao2998

Leong, C., Pan, S., Ractham, P., & Kaewkitipong, L. (2015). ICT-enabled community empowerment in crisis response: social media in Thailand flooding 2011. Journal of the Association for Information Systems, 16 (3), 174–212. https://doi.org/10.17705/1jais.00390 .

Lukyanenko, R., Parsons, J., & Wiersma, Y. F. (2014). The IQ of the crowd: understanding and improving information quality in structured user-generated content. Information Systems Research, 25 (4), 669–689. https://doi.org/10.1287/isre.2014.0537 .

Lundmark, L., Oh, C., & Verhaal, J. (2017). A little Birdie told me: Social media, organizational legitimacy, and underpricing in initial public offerings. Information Systems Frontiers, 19 (6), 1407–1422. https://doi.org/10.1007/s10796-016-9654-x .

Lyon, T. P., & Montgomery, A. W. (2015). The means and end of greenwash. Organization & Environment, 28 (2), 223–249. https://doi.org/10.1177/1086026615575332 .

Maier, C., Laumer, S., Eckhardt, A., & Weitzel, T. (2015). Giving too much social support: social overload on social networking sites. European Journal of Information Systems, 24 (5), 447–464. https://doi.org/10.1057/ejis.2014.3 .

Malik, A., Froese, F. J., & Sharma, P. (2020). Role of HRM in knowledge integration: Towards a conceptual framework. Journal of Business Research, 109, 524–535. https://doi.org/10.1016/j.jbusres.2019.01.029 .

Manski, C. F. (1993). Identification of endogenous social effects: the reflection problem. The Review of Economic Studies, 60 (3), 531–542. https://doi.org/10.2307/2298123 .

Massari, L. (2010). Analysis of MySpace user profiles. Information Systems Frontiers, 12 (4), 361–367. https://doi.org/10.1007/s10796-009-9206-8 .

Matook, S., Cummings, J., & Bala, H. (2015). Are you feeling lonely? The impact of relationship characteristics and online social network features on loneliness. Journal of Management Information Systems, 31 (4), 278–310.

Mettler, T., & Winter, R. (2016). Are business users social? A design experiment exploring information sharing in enterprise social systems. Journal of Information Technology, 31 (2), 101–114. https://doi.org/10.1057/jit.2015.28 .

Miller, A. R., & Tucker, C. (2013). Active social media management: the case of health care. Information Systems Research, 24 (1), 52–70. https://doi.org/10.1287/isre.1120.0466 .

Miranda, S. M., Kim, I., & Summers, J. D. (2015). Jamming with social media: How cognitive structuring of organizing vision facets affects it innovation diffusion. Mis Quarterly, 39 (3), 591. https://doi.org/10.25300/MISQ/2015/39.3.04 .

Miranda, S. M., Young, A., & Yetgin, E. (2016). Are social media emancipatory or hegemonic? Societal effects of mass media digitization in the case of the sopa discourse. Mis Quarterly, 40 (2), 303. https://doi.org/10.25300/MISQ/2016/40.2.02 .

Modgil, S., Singh, R. K., Gupta, S., & Dennehy, D. (2021). A confirmation bias view on social media induced polarisation during Covid-19.  Information Systems Frontiers . https://doi.org/10.1007/s10796-021-10222-9 .

Nagi, K. (2020). From bits and bytes to big data-An historical overview . Available at SSRN 3622921.

Nerur, S. P., Rasheed, A. A., & Natarajan, V. (2008). The intellectual structure of the strategic management field: an author co-citation analysis. Strategic Management Journal, 29 (3), 319–336. https://doi.org/10.1002/smj.659 .

Oestreicher-Singer, G., & Zalmanson, L. (2013). Content or community? A digital business strategy for content providers in the social age.(Special Issue: Digital Business Strategy)(Report). Mis Quarterly, 37 (2), 591. https://doi.org/10.25300/MISQ/2013/37.2.12 .

Orso, D., Federici, N., Copetti, R., Vetrugno, L., & Bove, T. (2020). Infodemic and the spread of fake news in the COVID-19-era.  European Journal of Emergency Medicine

Pan, Z., Lu, Y., Wang, B., & Chau, P. Y. K. (2017). Who do you think you are? Common and differential effects of social self-identity on social media usage. Journal of Management Information Systems, 34 (1), 71–101.

Parra, C. M., Gupta, M., & Dennehy, D. (2021). Likelihood of questioning ai-based recommendations due to perceived racial/gender bias. IEEE Transactions on Technology and Society

Pennycook, G., McPhetres, J., Zhang, Y., Lu, J. G., & Rand, D. G. (2020). Fighting COVID-19 misinformation on social media: Experimental evidence for a scalable accuracy-nudge intervention. Psychological Science, 31 (7), 770–780.

Piccialli, F., di Cola, V. S., Giampaolo, F., & Cuomo, S. (2021). The role of artificial intelligence in fighting the COVID-19 pandemic. Information Systems Frontiers, 23 (6), 1467–1497. https://doi.org/10.1007/s10796-021-10131-x .

Pierri, F., Artoni, A., & Ceri, S. (2020). Investigating Italian disinformation spreading on Twitter in the context of 2019 European elections. Plos One, 15 (1), e0227821. https://doi.org/10.1371/journal.pone.0227821 .

Posetti, J., & Matthews, A. (2018). A short guide to the history of ‘fake news’ and disinformation.  International Center For Journalists , 2018–2007

Preti, A., & Miotto, P. (2011). Self-deception, social desirability, and psychopathology. Behavioral and Brain Sciences, 34 (1), 37–37. https://doi.org/10.1017/S0140525X10002487 .

Prosser, C., Fieldhouse, E., Green, J., Mellon, J., & Evans, G. (2020). Tremors but no Youthquake: Measuring changes in the age and turnout gradients at the 2015 and 2017 British general elections. Electoral Studies, 64 . https://doi.org/10.1016/j.electstud.2020.102129 .

Ragin, C. (2013). New directions in the logic of social inquiry. Political Research Quarterly, 66 (1), 171–174.

Ragin, C. C. (2009). Qualitative comparative analysis using fuzzy sets (fsQCA). Configurational comparative methods: Qualitative comparative analysis (QCA) and related techniques , 51 , 87-121

Ragin, C. C., & Pennings, P. (2005). Fuzzy sets and social research. Sociological Methods & Research, 33 (4), 423–430. https://doi.org/10.1177/0049124105274499 .

Roozenbeek, J., & van der Linden, S. (2019). The fake news game: actively inoculating against the risk of misinformation. Journal of Risk Research, 22 (5), 570–580. https://doi.org/10.1080/13669877.2018.1443491 .

Rubin, V. L. (2019). Disinformation and misinformation triangle. Journal of Documentation, 75 (5), 1013–1034. https://doi.org/10.1108/JD-12-2018-0209 .

Scholthof, K. B. G. (2007). The disease triangle: pathogens, the environment and society. Nature Reviews Microbiology, 5 (2), 152–156.

Sommariva, S., Vamos, C., Mantzarlis, A., Đào, L. U. L., & Martinez Tyson, D. (2018). Spreading the (fake) news: exploring health messages on social media and the implications for health professionals using a case study. American Journal of Health Education, 49 (4), 246–255. https://doi.org/10.1080/19325037.2018.1473178 .

Tandoc, E. C., Jenkins, J., & Craft, S. (2019). Fake news as a critical incident in journalism. Journalism Practice, 13 (6), 673–689. https://doi.org/10.1080/17512786.2018.1562958 .

Tandoc, E. C., Lim, Z. W., & Ling, R. (2018). Defining “fake news.” Digital Journalism, 6 (2), 137–153. https://doi.org/10.1080/21670811.2017.1360143 .

Tran, T., Valecha, R., Rad, P., & Rao, H. R. (2021). An investigation of misinformation harms related to social media during two humanitarian crises. Information Systems Frontiers, 23 (4), 931–939. https://doi.org/10.1007/s10796-020-10088-3 .

Union, U. (2020). UN tackles ‘infodemic’ of misinformation and cybercrime in COVID-19 crisis .  https://www.un.org/en/un-coronavirus-communications-team/un-tackling-%E2%80%98infodemic%E2%80%99-misinformation-and-cybercrime-covid-19 . Accessed 19 May 2020.

Venkatraman, S., Cheung, M. K., Lee, C., Davis, Z. W. Y. D., & Venkatesh, V. (2018). The “Darth” side of technology use: an inductively derived typology of cyberdeviance. Journal of Management Information Systems, 35 (4), 1060–1091. https://doi.org/10.1080/07421222.2018.1523531 .

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 .

Wang, Y., Li, Y., & Luo, J. (2016). Deciphering the 2016 US Presidential campaign in the Twitter sphere: A comparison of the Trumpists and Clintonists . Tenth International AAAI Conference on Web and Social Media

Download references

Author information

Authors and affiliations.

Newcastle Business School, Northumbria University, Newcastle Upon Tyne, UK

Femi Olan & Emmanuel Ogiemwonyi Arakpogun

School of Business and Economics, Loughborough University, Loughborough, UK

Uchitha Jayawickrama

NIHR Newcastle IVD Co-operative Translational and Clinical Research Institute, Newcastle University, Newcastle Upon Tyne, UK

Jana Suklan

Plymouth Business School, University of Plymouth, Plymouth, UK

Shaofeng Liu

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Femi Olan .

Ethics declarations

Conflict of interest.

There is no conflict of interest and no funding was received for conducting this study. Also, All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this study.

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 4.0 International License, which permits use, sharing, adaptation, 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 changes were made. 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/4.0/ .

Reprints and permissions

About this article

Olan, F., Jayawickrama, U., Arakpogun, E.O. et al. Fake news on Social Media: the Impact on Society. Inf Syst Front 26 , 443–458 (2024). https://doi.org/10.1007/s10796-022-10242-z

Download citation

Accepted : 05 January 2022

Published : 19 January 2022

Issue Date : April 2024

DOI : https://doi.org/10.1007/s10796-022-10242-z

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

• Misinformation
• Societal acceptance
• Social media
• Societal values
• Find a journal
• Publish with us
• Track your research

Home — Essay Samples — Sociology — Sociology of Media and Communication — Fake News

Essays on Fake News

📰 fake news: time to bust some myths.

Hey there, folks! You've heard about fake news, right? It's everywhere these days. So, why bother writing an essay about it? Well, it's not just a school assignment; it's a chance to understand the power and perils of information in the digital age. Let's dive into the world of fake news and learn how to spot it like a pro! 🕵️‍♂️

📝 Fake News Essay Topics: Unraveling the Web of Deception

Picking a cool topic for your fake news essay is essential. You want to explore something that matters and makes you think critically. Check out these ideas:

🚀 The Spread of Fake News

Understanding how fake news spreads is crucial. Here are some essay topics:

• Social media's role in the rapid dissemination of fake news.
• How echo chambers and confirmation bias contribute to the spread of misinformation.
• The psychology behind why people believe and share fake news.
• Case studies of notable instances where fake news went viral.

💥 Impact and Consequences

Fake news doesn't just disappear; it has real-world consequences. Explore these essay topics:

• The influence of fake news on public opinion and elections.
• The economic and social costs of misinformation on society.
• How fake news affects trust in media and institutions.
• Case studies of incidents where fake news led to harmful actions.

🔍 Detection and Prevention

Knowing how to spot and combat fake news is essential. Consider these essay ideas:

• Tips and tools for identifying fake news and fact-checking.
• The role of media literacy and education in preventing the spread of misinformation.
• Strategies employed by tech companies and platforms to combat fake news.
• Case studies of successful campaigns against fake news and disinformation.

🤨 Ethical and Legal Aspects

There's an ethical and legal dimension to fake news. Dive into these essay topics:

• The balance between freedom of speech and the regulation of fake news.
• The responsibilities of social media platforms in curbing fake news.
• Exploring the ethical obligations of journalists and content creators in the age of misinformation.
• Case studies of legal actions taken against purveyors of fake news.

✍️ Fake News Essay Example

📜 thesis statement examples.

1. "Fake news is a pervasive issue in today's digital landscape, with profound consequences for democracy, society, and individual well-being. This essay delves into the mechanisms of fake news, its impact on public discourse, and the strategies we can employ to combat its spread."

2. "Misinformation and fake news thrive in the digital age, exploiting human psychology and technological platforms. Understanding their reach, influence, and methods of prevention is essential for safeguarding our information ecosystem and democratic values."

3. "As the boundaries between fact and fiction blur in the online realm, it becomes imperative to equip individuals with the skills to distinguish truth from falsehood. This essay explores the role of media literacy, technological solutions, and ethical considerations in mitigating the dangers of fake news."

4. "The power of misinformation extends beyond individual belief; it shapes societies and drives actions with far-reaching consequences. This essay investigates the ethical dilemmas, legal challenges, and educational imperatives surrounding the fight against fake news."

📝 Fake News Essay Introduction Paragraph Examples

1. "In a world where information flows like a river, fake news has become a formidable adversary. It's not just a buzzword; it's a force that can shape opinions, sway elections, and sow discord. This essay dives headfirst into the murky waters of fake news, aiming to uncover its secrets and empower you to navigate this digital wilderness."

2. "The age of the internet has brought incredible access to information, but it has also birthed a shadowy twin—fake news. It's more than just a harmless hoax; it's a threat to our collective understanding of reality. As we embark on this essay, we'll unravel the web of deception that surrounds fake news and equip ourselves with the tools to discern fact from fiction."

3. "Fake news is like a virus that spreads unchecked through the veins of our digital world. Its effects ripple through society, from the erosion of trust to the manipulation of public discourse. This essay is your guide to understanding the phenomenon of fake news, dissecting its impact, and exploring the strategies needed to inoculate our information ecosystem."

🔚 Fake News Essay Conclusion Paragraph Examples

1. "In closing, fake news is a formidable foe, but it's one we can combat with knowledge, critical thinking, and ethical responsibility. The consequences of misinformation are too significant to ignore. Let's pledge to be vigilant consumers of information and defenders of truth, ensuring that our digital world remains a place where facts prevail."

2. "As we wrap up this essay on fake news, remember that our collective awareness and actions matter. We can't eliminate fake news entirely, but we can mitigate its impact. By championing media literacy, demanding accountability from platforms, and upholding the values of truth and integrity, we can navigate the digital landscape with confidence."

3. "The battle against fake news is ongoing, but it's a battle worth fighting. It challenges our principles of truth, ethics, and democracy. Let this essay serve as a call to action—a reminder that we all play a part in preserving the integrity of information and protecting the foundations of our society."

Determining The Credibility of Evidence and Resources

Deepfakes and fake news, made-to-order essay as fast as you need it.

Each essay is customized to cater to your unique preferences

+ experts online

The Impact of Fake News

Impact on fake news, the negative impact of fake news on our society and individual mind, fake news in today's world, let us write you an essay from scratch.

• 450+ experts on 30 subjects ready to help
• Custom essay delivered in as few as 3 hours

How We Can Learn to Reject Fake News in The Digital World

Fake news and its negative consequences, the social issue of fake news and its effects on society, the problem of creating fake news in social media and its impact on society, get a personalized essay in under 3 hours.

Expert-written essays crafted with your exact needs in mind

Fake News in The Media

How fake news spreads like a real virus, the spread and recognition of fake news for students, what students can do to stop fake news and disinformation in the philippines’ media, how americans can help stop fake news and misinformation, fake news: a new platform in the modern era, government actions to combat online falsehood and fake news, publishing fake news: lying in journalism, creating of fake news in the american news media, the press’s role in the post-truth era and the world of mobile domination, american journalism and the problems it faces, research of the views of nietzsche and baudrillard on fake news, nigeria’s democracy in the era of fake news, the issues of the free speech protesters and the existence of racism throughout the history of america, the influence of the development of new media on british politics, an introduction to the daily show with jon stewart, an analysis of the american news and late-night talk show program, the daily show, the influence of fake news and fake followers on marketers, fact checking clown purge analysis, the impact of fake news on social media.

Fake news refers to deliberately fabricated or misleading information presented as factual news or journalism. It encompasses the dissemination of false or misleading content through various mediums, such as online platforms, social media, traditional media, or word-of-mouth. The purpose behind fake news is often to deceive, manipulate public opinion, or generate sensationalism for personal gain or ideological reasons.

Fake news continues to be a pressing issue in the United States, spreading misinformation and influencing public opinion. Here are some examples of fake news stories that have circulated in the US: Pizzagate: In 2016, a conspiracy theory emerged claiming that a Washington D.C. pizzeria was operating a child sex trafficking ring involving prominent politicians. The baseless allegations led to a man entering the restaurant with a firearm, highlighting the dangerous consequences of fake news. 2016 Election Misinformation: During the 2016 US presidential election, fake news stories spread widely on social media platforms. One notable example was the false claim that Pope Francis endorsed Donald Trump, which gained significant traction online, despite having no basis in reality. COVID-19 Misinformation: Throughout the COVID-19 pandemic, numerous fake news stories emerged, including false information about the origins of the virus, ineffective treatments, and conspiracy theories. Such misinformation has hindered public health efforts and undermined trust in authoritative sources.

The origin of fake news can be traced back to the advent of print media and the spread of misinformation throughout history. However, in recent years, the term "fake news" has gained significant attention due to its widespread dissemination through digital platforms and social media. The historical context of fake news is closely tied to the evolution of media and communication technologies. With the rise of the internet and social media platforms, anyone can create and share content, blurring the lines between reliable information and falsehoods. The speed and reach of digital communication have amplified the impact of fake news, making it a pressing issue in today's society. Various factors contribute to the spread of fake news, including political agendas, profit motives, and the manipulation of public opinion. In the age of information overload, distinguishing between accurate and false information has become increasingly challenging for individuals.

Fabricated Stories: These are completely made-up news stories designed to deceive readers. They often have catchy headlines and sensational claims, targeting people's emotions and capturing their attention. Misleading Content: This type of fake news involves presenting information out of context or selectively omitting details to manipulate the narrative. By distorting facts or presenting biased perspectives, misleading content can shape public opinion and deceive readers. Satire and Parody: Satirical news articles or parody websites are created for entertainment purposes, but they can sometimes be mistaken as real news. Although they are intended to be humorous or ironic, their content may be misconstrued as factual. Manipulated Images and Videos: Visual misinformation involves altering or manipulating images and videos to mislead viewers. This can include photoshopped images, doctored videos, or deepfakes, which use artificial intelligence to create realistic but fabricated media. Clickbait: Clickbait headlines are designed to grab attention and generate website traffic. They often exaggerate or sensationalize stories, luring readers to click on the link. While not always fake news, clickbait headlines can mislead readers by oversimplifying or distorting information.

Misinformation Campaigns: Fake news can be used as a tool for spreading false information to advance specific agendas or ideologies. It can be employed by individuals, groups, or organizations seeking to shape public opinion, influence elections, or sow discord within societies. Clickbait and Profit: Some creators of fake news aim to generate web traffic and earn advertising revenue. Sensational headlines and false stories are designed to grab attention and attract clicks, maximizing ad impressions and potential revenue. Propaganda and Disinformation: Fake news can be employed by governments, political parties, or interest groups to manipulate public opinion, discredit opponents, or create confusion. It can be used as a strategic tool to shape narratives and control information flows. Satire and Parody: While not necessarily intended to deceive, fake news in the form of satire or parody aims to entertain or critique through exaggerated or fictional stories. However, it can sometimes be misconstrued as genuine news, leading to unintended consequences. Personal or Social Malice: Individuals may create and spread fake news with the intention of harming others, settling personal scores, or causing social unrest. This can include false allegations, fabricated stories, or malicious hoaxes.

Concern and Distrust: Many people are increasingly concerned about the prevalence of fake news and its potential impact on society. They view it as a threat to the credibility of news media and the democratic process. As a result, there is a growing distrust in the information presented in news sources. Skepticism and Critical Thinking: The rise of fake news has led to an increased emphasis on critical thinking and fact-checking. People are becoming more cautious about accepting information at face value and are seeking reliable sources to verify the accuracy of news stories. Polarization and Confirmation Bias: Fake news can exacerbate existing divisions within society by targeting specific ideological groups. Some individuals may be more susceptible to believing and sharing fake news that aligns with their preconceived beliefs, contributing to echo chambers and reinforcing confirmation bias. Media Literacy and Education: There is a recognition of the importance of media literacy and education to combat fake news. Many individuals are advocating for improved digital literacy skills, teaching critical evaluation of sources, and promoting fact-checking as essential tools to navigate the information landscape. Call for Action: Some people are calling for regulatory measures, media transparency, and increased accountability to address the spread of fake news. They believe that platforms, governments, and news organizations should take responsibility for curbing misinformation and promoting accurate reporting.

The Momo Challenge: In 2018, rumors circulated on social media about the "Momo Challenge," a supposed online game encouraging self-harm and dangerous activities. The viral hoax created panic among parents and children, although there was no concrete evidence of its existence. Bowling Green Massacre: In 2017, a senior advisor to former President Donald Trump referred to a nonexistent "Bowling Green Massacre" to justify a travel ban. The event was fabricated, causing widespread confusion and criticism. War of the Worlds Radio Broadcast: In 1938, Orson Welles' radio adaptation of H.G. Wells' "War of the Worlds" caused panic among listeners who believed the fictional alien invasion was real. The broadcast highlighted the power of media to deceive and manipulate public perception. The Jayson Blair Scandal: In 2003, Jayson Blair, a journalist for The New York Times, was exposed for fabricating stories, plagiarizing content, and deceiving the public. This scandal highlighted the importance of media ethics and the need for fact-checking to combat fake news within established news organizations.

1. According to a study by Pew Research Center, 64% of adults in the United States believe that fake news has caused "a great deal" or "a fair amount" of confusion about basic facts of current events. 2. A research study by Massachusetts Institute of Technology (MIT) found that false information spreads six times faster on social media platforms like Twitter than true information. 3. A survey conducted by Ipsos in 27 countries revealed that nearly 60% of respondents said they had accidentally shared fake news or misinformation on social media. 4. The term "fake news" was named the Collins Dictionary's Word of the Year in 2017, reflecting its widespread use and impact on society. 5. In 2016, the Oxford Dictionaries declared "post-truth" as the Word of the Year, emphasizing the growing prevalence of fake news in public discourse. 6. The rise of fake news has led to increased efforts in fact-checking, with organizations like Snopes, FactCheck.org, and PolitiFact dedicated to debunking false claims and misinformation.

The topic of fake news is of utmost importance to write an essay about due to its significant impact on society, democracy, and the flow of information. In today's digital age, where information spreads rapidly and easily, distinguishing between factual news and misinformation has become increasingly challenging. Fake news has the potential to distort public opinion, undermine trust in credible sources, and even manipulate political processes. Exploring this topic allows for a critical examination of the factors contributing to the creation and dissemination of fake news, such as social media algorithms, echo chambers, and the profit-driven nature of online platforms. It also opens up discussions on the implications of fake news on individual decision-making, public discourse, and the erosion of democratic values. By analyzing the origins, types, effects, and responses to fake news, an essay on this topic helps raise awareness and promotes media literacy, emphasizing the importance of critical thinking and fact-checking in the digital era.

1. Allcott, H., & Gentzkow, M. (2017). Social media and fake news in the 2016 election. Journal of Economic Perspectives, 31(2), 211-236. 2. Berger, J. (2018). Contagious: How to build word of mouth in the digital age. Simon & Schuster. 3. Bovet, A., & Makse, H. A. (2019). Influence of fake news in Twitter during the 2016 US presidential election. Nature Communications, 10(1), 1-9. 4. Guess, A., Nagler, J., & Tucker, J. (2019). Less than you think: Prevalence and predictors of fake news dissemination on Facebook. Science Advances, 5(1), eaau4586. 5. Lewandowsky, S., Ecker, U. K., & Cook, J. (2017). Beyond Misinformation: Understanding and coping with the “post-truth” era. Journal of Applied Research in Memory and Cognition, 6(4), 353-369. 6. Lewandowsky, S., Ecker, U. K., & Cook, J. (Eds.). (2020). The debunking handbook 2020: Myths and facts about myths and facts. University of Bristol. 7. Pennycook, G., & Rand, D. G. (2019). The Implied Truth Effect: Attaching warnings to a subset of fake news stories increases perceived accuracy of stories without warnings. Management Science, 66(11), 4944-4957. 8. Roozenbeek, J., & van der Linden, S. (2019). The fake news game: actively inoculating against the risk of misinformation. Journal of Risk Research, 22(5), 570-580. 9. Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Science, 359(6380), 1146-1151. 10. Wardle, C., & Derakhshan, H. (2017). Information disorder: Toward an interdisciplinary framework for research and policy making. Council of Europe.

Relevant topics

• Social Media
• Effects of Social Media
• Media Analysis
• Sociological Imagination
• Discourse Community
• Social Justice
• Sex, Gender and Sexuality
• Cultural Appropriation
• American Identity

By clicking “Check Writers’ Offers”, you agree to our terms of service and privacy policy . We’ll occasionally send you promo and account related email

No need to pay just yet!

Bibliography

We use cookies to personalyze your web-site experience. By continuing we’ll assume you board with our cookie policy .

• Instructions Followed To The Letter
• Deadlines Met At Every Stage
• Unique And Plagiarism Free

• school Campus Bookshelves
• menu_book Bookshelves
• perm_media Learning Objects
• login Login
• how_to_reg Request Instructor Account
• hub Instructor Commons
• Download Page (PDF)
• Download Full Book (PDF)
• Periodic Table
• Physics Constants
• Scientific Calculator
• Reference & Cite
• Tools expand_more
• Readability

selected template will load here

This action is not available.

9.3: The Argumentative Essay

• Last updated
• Save as PDF
• Page ID 58378
• Lumen Learning

Learning Objectives

• Examine types of argumentative essays

Argumentative Essays

You may have heard it said that all writing is an argument of some kind. Even if you’re writing an informative essay, you still have the job of trying to convince your audience that the information is important. However, there are times you’ll be asked to write an essay that is specifically an argumentative piece.

An argumentative essay is one that makes a clear assertion or argument about some topic or issue. When you’re writing an argumentative essay, it’s important to remember that an academic argument is quite different from a regular, emotional argument. Note that sometimes students forget the academic aspect of an argumentative essay and write essays that are much too emotional for an academic audience. It’s important for you to choose a topic you feel passionately about (if you’re allowed to pick your topic), but you have to be sure you aren’t too emotionally attached to a topic. In an academic argument, you’ll have a lot more constraints you have to consider, and you’ll focus much more on logic and reasoning than emotions.

Argumentative essays are quite common in academic writing and are often an important part of writing in all disciplines. You may be asked to take a stand on a social issue in your introduction to writing course, but you could also be asked to take a stand on an issue related to health care in your nursing courses or make a case for solving a local environmental problem in your biology class. And, since argument is such a common essay assignment, it’s important to be aware of some basic elements of a good argumentative essay.

When your professor asks you to write an argumentative essay, you’ll often be given something specific to write about. For example, you may be asked to take a stand on an issue you have been discussing in class. Perhaps, in your education class, you would be asked to write about standardized testing in public schools. Or, in your literature class, you might be asked to argue the effects of protest literature on public policy in the United States.

However, there are times when you’ll be given a choice of topics. You might even be asked to write an argumentative essay on any topic related to your field of study or a topic you feel that is important personally.

Whatever the case, having some knowledge of some basic argumentative techniques or strategies will be helpful as you write. Below are some common types of arguments.

Causal Arguments

• In this type of argument, you argue that something has caused something else. For example, you might explore the causes of the decline of large mammals in the world’s ocean and make a case for your cause.

Evaluation Arguments

• In this type of argument, you make an argumentative evaluation of something as “good” or “bad,” but you need to establish the criteria for “good” or “bad.” For example, you might evaluate a children’s book for your education class, but you would need to establish clear criteria for your evaluation for your audience.

Proposal Arguments

• In this type of argument, you must propose a solution to a problem. First, you must establish a clear problem and then propose a specific solution to that problem. For example, you might argue for a proposal that would increase retention rates at your college.

Narrative Arguments

• In this type of argument, you make your case by telling a story with a clear point related to your argument. For example, you might write a narrative about your experiences with standardized testing in order to make a case for reform.

Rebuttal Arguments

• In a rebuttal argument, you build your case around refuting an idea or ideas that have come before. In other words, your starting point is to challenge the ideas of the past.

Definition Arguments

• In this type of argument, you use a definition as the starting point for making your case. For example, in a definition argument, you might argue that NCAA basketball players should be defined as professional players and, therefore, should be paid.

https://assessments.lumenlearning.co...essments/20277

Essay Examples

• Click here to read an argumentative essay on the consequences of fast fashion . Read it and look at the comments to recognize strategies and techniques the author uses to convey her ideas.
• In this example, you’ll see a sample argumentative paper from a psychology class submitted in APA format. Key parts of the argumentative structure have been noted for you in the sample.

Link to Learning

For more examples of types of argumentative essays, visit the Argumentative Purposes section of the Excelsior OWL .

Contributors and Attributions

• Argumentative Essay. Provided by : Excelsior OWL. Located at : https://owl.excelsior.edu/rhetorical-styles/argumentative-essay/ . License : CC BY: Attribution
• Image of a man with a heart and a brain. Authored by : Mohamed Hassan. Provided by : Pixabay. Located at : pixabay.com/illustrations/decision-brain-heart-mind-4083469/. License : Other . License Terms : pixabay.com/service/terms/#license

ESSAY SAUCE

FOR STUDENTS : ALL THE INGREDIENTS OF A GOOD ESSAY

Essay: Impact of fake news (focus on Philippines)

Essay details and download:.

• Subject area(s): Media essays
• Reading time: 3 minutes
• Price: Free download
• Published: 28 February 2022*
• File format: Text
• Words: 900 (approx)
• Number of pages: 4 (approx)
• Tags: Fake news essays

Text preview of this essay:

This page of the essay has 900 words. Download the full version above.

In this century, where innovations and advancement of technology is prominent, there are also issues and problems that comes along with it. As technology continues to develop, social media plays a role in educating and voicing out the concerns of millions of people. Social media can be associated with a double-edged knife. We benefit from accessing information quickly, and there is no limitation on who we can connect to. It is one way of interacting with other people to share thoughts and ideas with. However, what will happen if the information we are getting are no longer truthful? It is truly necessary to be aware and vigilant of the things we see online and confirm these first before believing in them. Indeed, fake news has its negative effects on building a sense of nationalism among Filipinos.

Fake news are stories that are false wherein the story itself is fabricated, with no verifiable sources, quotes, or facts. In line with this, nationalism is the sense of unity among citizens and it is a movement that promotes a nation’s best interest and it is greatly affected by the spread of fake news in the country. In building our sense of nationalism, we have to be one, as citizens, and our democracy shouldn’t be tainted.

Incidentally, the media is often accused of spreading fake news . There is growing evidence on attacks on media credibility and a sharp increase in the number of journalists imprisoned on false news charges (Lees, 2018). In the Philippines, President Duterte labelled a particular media outlet as “fake news” and even announced that he would revoke said media’s operating license. This is really alarming since the President had no evidence on such accusations and felt personally attacked by the published articles of the media outlet which caused his outburst. These attacks on press freedom has its impact on the society at large. It is the job of the media to report necessary and relevant information to the people as this would help them see and understand the happenings in the society. According to Civicus (n.d.), when authorities discredit the media, disagreements happen between citizens which may result to protests and violence. These fake news against media will prevent Filipinos from strengthening their sense of nationalism since they are fighting their own fellowmen and it is opposite to the essence of nationalism which is to create unity among citizens.

Aside from this, sharing of fake news is not only limited to the media, a lot of trolls on social media are beginning to multiply and the number of people who actually believe in this fake news is troubling. This is because people are believing more in this fake information rather than credible sources and they would make decisions based on false assumptions (Quilinguing, 2019). One example of this situation is the Digong Duterte Supporters (DDS), as the pandemic continues to take its toll, these supporters keeps on creating heated arguments on social media. They keep on sharing false information about other politicians who disagree with President Duterte’s actions. On the other hand, it also finds its way to affect democracy in the Philippines. Since social media is a way for Filipinos to have a voice, with the presence of fake online accounts, they can be silenced by the latter. They may manipulate public opinion to favor the interests of a particular group (Quilinguing, 2019). In this case, the oppressed remains oppressed and they lose their freedom in making their sentiments heard. This prevents the people from building their nationalism as they are being deprived of their freedom to speak their minds and it also causes conflicts in the people of society which contradicts the meaning of nationalism in terms of having unity.

Another impact of fake news that spreads through misinformation has its way in constructing stereotypes and further discrimination among a particular sector of the society. Grambo (2019) stresses that fake news is divisive as it may negatively portray ethnic or religious group as people who are unworthy of citizenship or they may even dehumanize these individuals. In the Philippines, there is an existing dueling groups: DDS and dilawan. Just because they do not agree with the actions done by the President, they are immediately labeled as such. In addition, these groups often exchange hurtful and personal attacks with each other. Fake news is usually the reason why both groups battle each other which in turn widens the gap between them. These situations only add fuel to the existing problems in the society and people would have no unity since they do not consider their fellowmen as their equal and they would have a hard time in building nationalism among themselves since its essence is to create oneness among the citizens and uphold the democracy of each individual.

Ultimately, nothing good comes out of spreading fake news. It will greatly impact how news are presented, how decisions are made, how people are treated, and it facilitates discrimination among Filipinos. To reiterate, it cannot be denied that social media has greatly benefited us in our daily lives. However, as fake news continues to progress, we must always check the credibility of the information we read before we believe them. We must inform and educate others as well if they become victims of these false information. To start, we should stop encouraging the spread of fake news.

2020-9-21-1600675349

...(download the rest of the essay above)

Discover more:

• Fake news essays

Related essays:

• Fake and biased news, and its impact on society
• Deepfakes – history, examples, uses and risks

Recommended for you

• The Effectiveness of Censoring Fake News: A Look at Singapore’s Approach
• Is the gatekeeper concept applicable for the journalistic practice in the Czech Republic?

About this essay:

If you use part of this page in your own work, you need to provide a citation, as follows:

Essay Sauce, Impact of fake news (focus on Philippines) . Available from:<https://www.essaysauce.com/media-essays/impact-of-fake-news-focus-on-philippines/> [Accessed 10-04-24].

These Media essays have been submitted to us by students in order to help you with your studies.

* This essay may have been previously published on Essay.uk.com at an earlier date.

Essay Categories:

• Accounting essays
• Architecture essays
• Business essays
• Computer science essays
• Criminology essays
• Economics essays
• Education essays
• Engineering essays
• English language essays
• Environmental studies essays
• Essay examples
• Finance essays
• Geography essays
• Health essays
• History essays
• Hospitality and tourism essays
• Human rights essays
• Information technology essays
• International relations
• Leadership essays
• Linguistics essays
• Literature essays
• Management essays
• Marketing essays
• Mathematics essays
• Media essays
• Medicine essays
• Military essays
• Miscellaneous essays
• Music Essays
• Nursing essays
• Philosophy essays
• Photography and arts essays
• Politics essays
• Project management essays
• Psychology essays
• Religious studies and theology essays
• Sample essays
• Science essays
• Social work essays
• Sociology essays
• Sports essays
• Types of essay
• Zoology essays

• Skip to main content
• Keyboard shortcuts for audio player

NPR defends its journalism after senior editor says it has lost the public's trust

David Folkenflik

NPR is defending its journalism and integrity after a senior editor wrote an essay accusing it of losing the public's trust. Saul Loeb/AFP via Getty Images hide caption

NPR is defending its journalism and integrity after a senior editor wrote an essay accusing it of losing the public's trust.

NPR's top news executive defended its journalism and its commitment to reflecting a diverse array of views on Tuesday after a senior NPR editor wrote a broad critique of how the network has covered some of the most important stories of the age.

"An open-minded spirit no longer exists within NPR, and now, predictably, we don't have an audience that reflects America," writes Uri Berliner.

A strategic emphasis on diversity and inclusion on the basis of race, ethnicity and sexual orientation, promoted by NPR's former CEO, John Lansing, has fed "the absence of viewpoint diversity," Berliner writes.

NPR's chief news executive, Edith Chapin, wrote in a memo to staff Tuesday afternoon that she and the news leadership team strongly reject Berliner's assessment.

"We're proud to stand behind the exceptional work that our desks and shows do to cover a wide range of challenging stories," she wrote. "We believe that inclusion — among our staff, with our sourcing, and in our overall coverage — is critical to telling the nuanced stories of this country and our world."

NPR names tech executive Katherine Maher to lead in turbulent era

She added, "None of our work is above scrutiny or critique. We must have vigorous discussions in the newsroom about how we serve the public as a whole."

A spokesperson for NPR said Chapin, who also serves as the network's chief content officer, would have no further comment.

Praised by NPR's critics

Berliner is a senior editor on NPR's Business Desk. (Disclosure: I, too, am part of the Business Desk, and Berliner has edited many of my past stories. He did not see any version of this article or participate in its preparation before it was posted publicly.)

Berliner's essay , titled "I've Been at NPR for 25 years. Here's How We Lost America's Trust," was published by The Free Press, a website that has welcomed journalists who have concluded that mainstream news outlets have become reflexively liberal.

Berliner writes that as a Subaru-driving, Sarah Lawrence College graduate who "was raised by a lesbian peace activist mother ," he fits the mold of a loyal NPR fan.

Yet Berliner says NPR's news coverage has fallen short on some of the most controversial stories of recent years, from the question of whether former President Donald Trump colluded with Russia in the 2016 election, to the origins of the virus that causes COVID-19, to the significance and provenance of emails leaked from a laptop owned by Hunter Biden weeks before the 2020 election. In addition, he blasted NPR's coverage of the Israel-Hamas conflict.

On each of these stories, Berliner asserts, NPR has suffered from groupthink due to too little diversity of viewpoints in the newsroom.

The essay ricocheted Tuesday around conservative media , with some labeling Berliner a whistleblower . Others picked it up on social media, including Elon Musk, who has lambasted NPR for leaving his social media site, X. (Musk emailed another NPR reporter a link to Berliner's article with a gibe that the reporter was a "quisling" — a World War II reference to someone who collaborates with the enemy.)

When asked for further comment late Tuesday, Berliner declined, saying the essay spoke for itself.

The arguments he raises — and counters — have percolated across U.S. newsrooms in recent years. The #MeToo sexual harassment scandals of 2016 and 2017 forced newsrooms to listen to and heed more junior colleagues. The social justice movement prompted by the killing of George Floyd in 2020 inspired a reckoning in many places. Newsroom leaders often appeared to stand on shaky ground.

Leaders at many newsrooms, including top editors at The New York Times and the Los Angeles Times , lost their jobs. Legendary Washington Post Executive Editor Martin Baron wrote in his memoir that he feared his bonds with the staff were "frayed beyond repair," especially over the degree of self-expression his journalists expected to exert on social media, before he decided to step down in early 2021.

Since then, Baron and others — including leaders of some of these newsrooms — have suggested that the pendulum has swung too far.

Author Interviews

Legendary editor marty baron describes his 'collision of power' with trump and bezos.

New York Times publisher A.G. Sulzberger warned last year against journalists embracing a stance of what he calls "one-side-ism": "where journalists are demonstrating that they're on the side of the righteous."

"I really think that that can create blind spots and echo chambers," he said.

Internal arguments at The Times over the strength of its reporting on accusations that Hamas engaged in sexual assaults as part of a strategy for its Oct. 7 attack on Israel erupted publicly . The paper conducted an investigation to determine the source of a leak over a planned episode of the paper's podcast The Daily on the subject, which months later has not been released. The newsroom guild accused the paper of "targeted interrogation" of journalists of Middle Eastern descent.

Heated pushback in NPR's newsroom

Given Berliner's account of private conversations, several NPR journalists question whether they can now trust him with unguarded assessments about stories in real time. Others express frustration that he had not sought out comment in advance of publication. Berliner acknowledged to me that for this story, he did not seek NPR's approval to publish the piece, nor did he give the network advance notice.

Some of Berliner's NPR colleagues are responding heatedly. Fernando Alfonso, a senior supervising editor for digital news, wrote that he wholeheartedly rejected Berliner's critique of the coverage of the Israel-Hamas conflict, for which NPR's journalists, like their peers, periodically put themselves at risk.

Alfonso also took issue with Berliner's concern over the focus on diversity at NPR.

"As a person of color who has often worked in newsrooms with little to no people who look like me, the efforts NPR has made to diversify its workforce and its sources are unique and appropriate given the news industry's long-standing lack of diversity," Alfonso says. "These efforts should be celebrated and not denigrated as Uri has done."

After this story was first published, Berliner contested Alfonso's characterization, saying his criticism of NPR is about the lack of diversity of viewpoints, not its diversity itself.

"I never criticized NPR's priority of achieving a more diverse workforce in terms of race, ethnicity and sexual orientation. I have not 'denigrated' NPR's newsroom diversity goals," Berliner said. "That's wrong."

Questions of diversity

Under former CEO John Lansing, NPR made increasing diversity, both of its staff and its audience, its "North Star" mission. Berliner says in the essay that NPR failed to consider broader diversity of viewpoint, noting, "In D.C., where NPR is headquartered and many of us live, I found 87 registered Democrats working in editorial positions and zero Republicans."

Berliner cited audience estimates that suggested a concurrent falloff in listening by Republicans. (The number of people listening to NPR broadcasts and terrestrial radio broadly has declined since the start of the pandemic.)

Former NPR vice president for news and ombudsman Jeffrey Dvorkin tweeted , "I know Uri. He's not wrong."

Others questioned Berliner's logic. "This probably gets causality somewhat backward," tweeted Semafor Washington editor Jordan Weissmann . "I'd guess that a lot of NPR listeners who voted for [Mitt] Romney have changed how they identify politically."

Similarly, Nieman Lab founder Joshua Benton suggested the rise of Trump alienated many NPR-appreciating Republicans from the GOP.

In recent years, NPR has greatly enhanced the percentage of people of color in its workforce and its executive ranks. Four out of 10 staffers are people of color; nearly half of NPR's leadership team identifies as Black, Asian or Latino.

"The philosophy is: Do you want to serve all of America and make sure it sounds like all of America, or not?" Lansing, who stepped down last month, says in response to Berliner's piece. "I'd welcome the argument against that."

"On radio, we were really lagging in our representation of an audience that makes us look like what America looks like today," Lansing says. The U.S. looks and sounds a lot different than it did in 1971, when NPR's first show was broadcast, Lansing says.

A network spokesperson says new NPR CEO Katherine Maher supports Chapin and her response to Berliner's critique.

The spokesperson says that Maher "believes that it's a healthy thing for a public service newsroom to engage in rigorous consideration of the needs of our audiences, including where we serve our mission well and where we can serve it better."

Disclosure: This story was reported and written by NPR Media Correspondent David Folkenflik and edited by Deputy Business Editor Emily Kopp and Managing Editor Gerry Holmes. Under NPR's protocol for reporting on itself, no NPR corporate official or news executive reviewed this story before it was posted publicly.