research questions on the use of social media

13 social media research topics to explore in 2024

Last updated

15 January 2024

Reviewed by

Miroslav Damyanov

Short on time? Get an AI generated summary of this article instead

To help you choose a specific area to examine, here are some of the top social media research topics that are relevant in 2024.

• What makes a strong social media research topic?

Consider the factors below to ensure your topic is strong and compelling:

Clarity: regardless of the topic you investigate, clarity is essential. It ensures readers will be able to understand your work and any wider learnings. Your argument should be clear and your language unambiguous.

Trend relevancy: you need to know what’s currently happening in social media to draw relevant conclusions. Before choosing a topic, consider current popular platforms, trending content, and current use cases to ensure you understand social media as it is today.

New insights: if your research is to be new, innovative, and helpful for the wider population, it should cover areas that haven’t been studied before. Look into what’s already been thoroughly researched to help you uncover knowledge gaps that could be good focus areas.

• Tips for choosing social media research topics

When considering social media research questions, it’s also important to consider whether you’re the right person to conduct that area of study. Your skills, interests, and time allocated will all impact your suitability.

Consider your skillset: your specific expertise is highly valuable when conducting research. Choosing a topic that aligns with your skills will help ensure you can add a thorough analysis and your own learnings.

Align with your interests: if you’re deeply interested in a topic, you’re much more likely to enjoy the process and dedicate the time it needs for a thorough analysis.

Consider your resources: the time you have available to complete the research, your allocated funds, and access to resources should all impact the research topic you choose.

• 13 social media research paper topics

To help you choose the right area of research, we’ve rounded up some of the most compelling topics within the sector. These ideas may also help you come up with your own.

1. The influence of social media on mental health

It’s well-documented that social media can impact mental health. For example, a significant amount of research has highlighted the link between social media and conditions like anxiety, depression, and stress—but there’s still more to uncover in this area.

There are high rates of mental illness worldwide, so there’s continual interest in ways to understand and mitigate it. Studies could focus on the following areas:

The reasons why social media can impact mental health

How social media can impact specific mental health conditions (you might also look at different age groups here)

How to reduce social media’s impact on mental health

2. The effects of social media exposure on child development

There are many unknowns with social media. More research is needed to understand how it impacts children. As such, this is a very valuable research area.

You might explore the following topics:

How social media impacts children at different ages

The long-term effects of childhood social media use

The benefits of social media use in children

How social media use impacts childhood socialization, communication, and learning

3. The role of social media in political campaigning

Social media’s role in political campaigning is nothing new. The Cambridge Analytica Scandal, for example, involved data from millions of Facebook profiles being sold to a third party for political advertising. Many believe this could have impacted the 2016 US election results. Ultimately, Facebook had to pay a private class-action lawsuit of $725 million.

The role of social media in political campaigns is of global significance. Concerns are still high that social media can play a negative role in elections due to the spread of misinformation, disinformation, and the bandwagon effect.

Research in this area could look into the following topics:

How people are influenced by social media when it comes to voting

Ways to mitigate misinformation

Election interference and how this can be prevented

4. The role of social media in misinformation and disinformation

Misinformation and disinformation mean slightly different things. Misinformation is unintentionally sharing false or inaccurate information, while disinformation is sharing false information with the deliberate intent to mislead people.

Both can play a role not just in elections but throughout social media. This became particularly problematic during the COVID-19 pandemic.

Research into this area is important given the widespread risk that comes with spreading false information about health and safety-related topics.

Here are some potential research areas:

How misinformation and disinformation are spread via social media

The impact of false information (you could focus on how it impacts health, for example)

Strategies for mitigating the impact of false information and encouraging critical thinking

The avenues through which to hold technology companies accountable for spreading misinformation

5. The impact of AI and deepfakes on social media 

AI technology is expected to continue expanding in 2024. Some are concerned that this could impact social media. One concern is the potential for the widespread use of deepfake technology—a form of AI that uses deep learning to create fake images.

Fake images can be used to discredit, shame, and control others, so researchers need to deeply understand this area of technology. You might look into the following areas:

The potential impacts of deepfakes on businesses and their reputations

Deepfake identities on social media: privacy concerns and other risks

How deepfake images can be identified, controlled, and prevented

6. How social media can benefit communities

While there’s much research into the potential negative impacts of social media, it can also provide many benefits.

Social media can establish connections for those who might otherwise be isolated in the community. It can facilitate in-person gatherings and connect people who are physically separated, such as relatives who live in different countries. Social media can also provide critical information to communities quickly in the case of emergencies.

Research into the ways social media can provide these key benefits can make interesting topics. You could consider the following:

Which social media platforms offer the most benefits

How to better use social media to lean into these benefits

How new social platforms could connect us in more helpful ways

7. The psychology of social media

Social media psychology explores human behavior in relation to social media. There are a range of topics within social media psychology, including the following: 

The influence of social media on social comparison

Addiction and psychological dependence on social media

How social media increases the risk of cyberbullying

How social media use impacts people’s attention spans

Social interactions and the impact on socialization

Persuasion and influence on social media

8. How communication has evolved through social media

Social media has provided endless ways for humans to connect and interact, so the ways we do this have evolved.

Most obviously, social media has provided ways to connect instantaneously via real-time messaging and communicate using multimedia formats, including text, images, emojis, video content, and audio.

This has made communication more accessible and seamless, especially given many people now own smartphones that can connect to social media apps from anywhere.

You might consider researching the following topics:

How social media has changed the way people communicate

The impacts of being continuously connected, both positive and negative

How communication may evolve in the future due to social media

9. Social media platforms as primary news sources

As social media use has become more widespread, many are accessing news information primarily from their newsfeeds. This can be particularly problematic, given that newsfeeds are personalized providing content to people based on their data.

This can cause people to live in echo chambers, where they are constantly targeted with content that aligns with their beliefs. This can cause people to become more entrenched in their way of thinking and more unable or unwilling to see other people’s opinions and points of view.

Research in this area could consider the following:

The challenges that arise from using social media platforms as a primary news source

The pros and cons of social media: does it encourage “soloization” or diverse perspectives?

How to prevent social media echo chambers from occurring

The impact of social media echo chambers on journalistic integrity

10. How social media is impacting modern journalism

News platforms typically rely on an advertising model where more clicks and views increase revenue. Since sensationalist stories can attract more clicks and shares on social media, modern journalism is evolving.

Journalists are often rewarded for writing clickbait headlines and content that’s more emotionally triggering (and therefore shareable).

Your research could cover the following areas:

How journalism is evolving due to social media

How to mitigate social media’s impact on neutral reporting

The importance of journalistic standards in the age of social media

11. The impact of social media on traditional advertising

Digital advertising is growing in popularity. Worldwide, ad spending on social media was expected to reach $207.1 billion in 2023 . Experts estimate that ad spending on mobile alone will reach $255.8 billion by 2028 . This move continues to impact traditional advertising, which takes place via channels like print, TV, and radio.

Most organizations consider their social strategy a critical aspect of their advertising program. Many exclusively advertise on social media—especially those with limited budgets.

Here are some interesting research topics in this area

The impact of different advertising methods

Which social media advertising channels provide the highest return on investment (ROI)

The societal impacts of social media advertising

12. Impacts of social media presence on corporate image

Social media presence can provide companies with an opportunity to be visible and increase brand awareness . Social media also provides a key way to interact with customers.

More and more customers now expect businesses to be online. Research shows that 63% of customers expect companies to offer customer service via their social media channels, while a whopping 90% have connected with a brand or business through social media.

Research in this area could focus on the following topics:

The advantages and disadvantages of social media marketing for businesses

How social media can impact a business’s corporate image

How social media can boost customer experience and loyalty

13. How social media impacts data privacy

Using social media platforms is free for the most part, but users have to provide their personal data for the privilege. This means data collection , tracking, the potential for third parties to access that data, psychological profiling, geolocation, and tracking are all potential risks for users.

Data security and privacy are of increasing interest globally. Research within this area will likely be in high demand in 2024.

Here are some of the research topics you might want to consider in this area:

Common privacy concerns with social media use

Why is social media privacy important?

What can individuals do to protect their data when using social media?

• The importance of social media research

As social media use continues to expand in the US and around the world, there’s continual interest in research on the topic. The research you conduct could positively impact many groups of people.

Topics can cover a broad range of areas. You might look at how social media can harm or benefit people, how social media can impact journalism, how platforms can impact young people, or the data privacy risks involved with social media use. The options are endless, and new research topics will present themselves as technology evolves.

Should you be using a customer insights hub?

Do you want to discover previous research faster?

Do you share your research findings with others?

Do you analyze research data?

Start for free today, add your research, and get to key insights faster

Editor’s picks

Last updated: 9 November 2024

Last updated: 30 January 2024

Last updated: 17 January 2024

Last updated: 30 April 2024

Last updated: 12 December 2023

Last updated: 4 July 2024

Last updated: 12 October 2023

Last updated: 6 March 2024

Last updated: 5 March 2024

Last updated: 31 January 2024

Last updated: 23 January 2024

Last updated: 13 May 2024

Last updated: 20 December 2023

Latest articles

Related topics, a whole new way to understand your customer is here, log in or sign up.

Get started for free

• Privacy Policy

Home » Social Media Research Topics

Social Media Research Topics

Table of Contents

Social media has transformed how we communicate, work, and interact with the world, making it a fertile ground for research. As a constantly evolving platform, social media touches various fields like psychology, business, politics, and technology. Research in this area helps us understand its effects on individuals, societies, and economies. This post explores key research topics in social media, providing ideas and insights for students, academics, and professionals interested in studying its dynamics.

Social media research helps us uncover insights into digital behavior, identify trends, and understand the societal impacts of these platforms. With over 4 billion users globally, social media has profound effects on identity, privacy, public opinion, and even mental health. Research in this area can guide best practices, inform policies, and shed light on how social media reshapes our daily lives.

Key Areas for Social Media Research

• Impact of Social Media on Self-Esteem and Body Image : Examines how curated images and content affect users’ self-perception and self-worth.
• Addiction and Dependency : Studies social media addiction, its behavioral patterns, and its impact on overall mental health.
• Cyberbullying and Online Harassment : Explores the psychological impact of bullying and harassment online, especially among young users.
• Effectiveness of Influencer Marketing : Analyzes how influencers shape consumer attitudes and purchasing behavior.
• Trust and Authenticity in Influencer-Consumer Relationships : Investigates how perceived authenticity influences consumer trust and loyalty.
• Role of Micro-Influencers in Marketing : Looks into the power of smaller influencers compared to celebrity endorsements in driving engagement.
• User Awareness of Privacy Risks : Studies the awareness and attitudes of users towards privacy risks on social media.
• Data Collection and Personalization : Examines how data is collected, how it’s used for targeted advertising, and the ethics surrounding it.
• Impact of Privacy Policy Changes on User Behavior : Investigates how updates in privacy policies (such as Facebook’s and WhatsApp’s) affect user engagement and trust.
• Social Media as a Tool for Political Campaigns : Analyzes how politicians and campaigns use social media to shape public opinion and influence elections.
• Spread of Misinformation and Fake News : Explores the role of social media in spreading misinformation and its effect on public trust.
• Echo Chambers and Polarization : Studies how algorithms can create echo chambers, leading to increased polarization on topics like politics and health.
• Role of Social Media in Public Health Crises : Investigates how social media has been used to communicate information during health crises like the COVID-19 pandemic.
• Government and Organization Use of Social Media in Emergencies : Analyzes how governments and organizations use platforms to share emergency updates.
• Public Response to Crisis Information : Studies how the public engages with and responds to crisis information shared online.
• Influence of Social Media on Teen Identity : Looks into how social media affects identity formation and self-image among adolescents.
• Presentation of Self on Social Media : Studies how users curate and present different aspects of their identities on social platforms.
• Gender and Social Media : Explores how gender representation and stereotypes are reinforced or challenged on social media platforms.
• Social Media as a Learning Tool : Investigates the effectiveness of platforms like YouTube and LinkedIn in providing educational content.
• Digital Literacy and Responsible Usage : Studies how digital literacy training can help students use social media responsibly.
• Impact of Social Media on Student Performance : Examines how the use of social media affects students’ academic performance and time management.
• Role of AI in Content Moderation : Looks into how AI is being used to moderate content, identify hate speech, and prevent the spread of misinformation.
• Rise of Augmented Reality (AR) in Social Media : Examines how AR features like filters and virtual try-ons are transforming user experiences on platforms like Instagram and Snapchat.
• Social Commerce and E-commerce Integration : Studies the role of social media as a direct sales platform and its effect on e-commerce.
• Responsibility of Platforms in Preventing Harm : Analyzes the role social media companies play in protecting users from harmful content.
• Ethical Implications of Social Media Algorithms : Studies how recommendation algorithms may affect user behavior and content consumption.
• Social Media’s Role in Shaping Cultural Values : Explores how platforms influence cultural norms, values, and social behavior.
• Social Media’s Impact on Relationships
• Effects on Romantic Relationships : Studies the impact of social media on romantic relationships, including jealousy, trust issues, and communication.
• Family Dynamics in the Age of Social Media : Investigates how social media affects family relationships and parent-child interactions.
• Friendship and Social Networks : Looks into how online friendships compare to offline ones and the role of social media in maintaining long-distance relationships.

How to Choose a Research Topic in Social Media

• Identify Your Interests Start by identifying which aspect of social media interests you most—whether it’s mental health, marketing, privacy, or any other area.
• Review Recent Studies Use resources like Google Scholar to review recent research in your area of interest. This will give you an idea of the latest findings and areas that need further exploration.
• Narrow Your Focus Social media is a broad field, so try to narrow your focus. Instead of researching “Social Media and Mental Health,” specify it to something like “The Impact of Instagram on Teenage Body Image.”
• Formulate a Research Question Once you have a specific area, formulate a research question that will guide your study. A clear research question will help you structure your investigation and provide a clear goal.
• Consider Ethical Implications Social media research often involves sensitive topics, so consider the ethical implications. Ensure your research respects privacy and adheres to ethical guidelines.

Example Research Questions

• How does the use of Instagram filters affect self-esteem among adolescents?
• What role does social media play in spreading misinformation during public health crises?
• How do social media algorithms impact political polarization and public opinion?
• What factors influence consumer trust in influencer recommendations?
• How do different age groups respond to privacy policies on social media platforms?

Social media research covers a broad range of topics with far-reaching implications. By studying the effects, trends, and ethical considerations surrounding social media, researchers can contribute valuable insights into this digital phenomenon that impacts every aspect of modern life. Whether you’re a student, an academic, or a professional, exploring these topics can provide a deeper understanding of how social media shapes individuals and society.

• Chen, G. M., & Zhang, Y. (2019). Social Media Research: Theories, Constructs, and Applications . Journal of Communication, 69(3), 370–389.
• Boyd, D., & Ellison, N. B. (2017). Social Network Sites: Definition, History, and Scholarship . Journal of Computer-Mediated Communication, 13(1), 210–230.
• Aral, S., Eckles, D., & Levandoski, J. (2020). The Impact of Social Media on Well-being and Social Comparison . Annual Review of Psychology, 71, 639-665.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Qualitative Research Titles and Topics

Astronomy Research Topics

Educational Research Topics

Argumentative Research Paper Topics

Political Science Research Topics

Psychology Research Paper Topics

Numbers, Facts and Trends Shaping Your World

Read our research on:

Full Topic List

Regions & Countries

• Publications
• Our Methods
• Short Reads
• Tools & Resources

Read Our Research On:

• Social Media Use in 2021

A majority of Americans say they use YouTube and Facebook, while use of Instagram, Snapchat and TikTok is especially common among adults under 30.

Table of contents.

• Acknowledgments
• Methodology

To better understand Americans’ use of social media, online platforms and messaging apps, Pew Research Center surveyed 1,502 U.S. adults from Jan. 25 to Feb. 8, 2021, by cellphone and landline phone. The survey was conducted by interviewers under the direction of Abt Associates and is weighted to be representative of the U.S. adult population by gender, race, ethnicity, education and other categories. Here are the  questions used for this report , along with responses, and  its methodology .

Despite a string of controversies and the public’s relatively negative sentiments about aspects of social media, roughly seven-in-ten Americans say they ever use any kind of social media site – a share that has remained relatively stable over the past five years, according to a new Pew Research Center survey of U.S. adults.

Beyond the general question of overall social media use, the survey also covers use of individual sites and apps. YouTube and Facebook continue to dominate the online landscape, with 81% and 69%, respectively, reporting ever using these sites. And YouTube and Reddit were the only two platforms measured that saw statistically significant growth since 2019 , when the Center last polled on this topic via a phone survey.

When it comes to the other platforms in the survey, 40% of adults say they ever use Instagram and about three-in-ten report using Pinterest or LinkedIn. One-quarter say they use Snapchat, and similar shares report being users of Twitter or WhatsApp. TikTok – an app for sharing short videos – is used by 21% of Americans, while 13% say they use the neighborhood-focused platform Nextdoor.

Even as other platforms do not nearly match the overall reach of YouTube or Facebook, there are certain sites or apps, most notably Instagram, Snapchat and TikTok, that have an especially strong following among young adults. In fact, a majority of 18- to 29-year-olds say they use Instagram (71%) or Snapchat (65%), while roughly half say the same for TikTok.

These findings come from a nationally representative survey of 1,502 U.S. adults conducted via telephone Jan. 25-Feb.8, 2021.

With the exception of YouTube and Reddit, most platforms show little growth since 2019

YouTube is the most commonly used online platform asked about in this survey, and there’s evidence that its reach is growing. Fully 81% of Americans say they ever use the video-sharing site, up from 73% in 2019. Reddit was the only other platform polled about that experienced statistically significant growth during this time period – increasing from 11% in 2019 to 18% today. 

Facebook’s growth has leveled off over the last five years, but it remains one of the most widely used social media sites among adults in the United States: 69% of adults today say they ever use the site, equaling the share who said this two years prior.  

Similarly, the respective shares of Americans who report using Instagram, Pinterest, LinkedIn, Snapchat, Twitter and WhatsApp are statistically unchanged since 2019 . This represents a broader trend that extends beyond the past two years in which the rapid adoption of most of these sites and apps seen in the last decade has slowed. (This was the first year the Center asked about TikTok via a phone poll and the first time it has surveyed about Nextdoor.)

Adults under 30 stand out for their use of Instagram, Snapchat and TikTok

When asked about their social media use more broadly – rather than their use of specific platforms – 72% of Americans say they ever use social media sites.

In a pattern consistent with past Center studies on social media use, there are some stark age differences. Some 84% of adults ages 18 to 29 say they ever use any social media sites, which is similar to the share of those ages 30 to 49 who say this (81%). By comparison, a somewhat smaller share of those ages 50 to 64 (73%) say they use social media sites, while fewer than half of those 65 and older (45%) report doing this.

These age differences generally extend to use of specific platforms, with younger Americans being more likely than their older counterparts to use these sites – though the gaps between younger and older Americans vary across platforms.

Majorities of 18- to 29-year-olds say they use Instagram or Snapchat and about half say they use TikTok, with those on the younger end of this cohort – ages 18 to 24 – being especially likely to report using Instagram (76%), Snapchat (75%) or TikTok (55%). 1 These shares stand in stark contrast to those in older age groups. For instance, while 65% of adults ages 18 to 29 say they use Snapchat, just 2% of those 65 and older report using the app – a difference of 63 percentage points.

Additionally, a vast majority of adults under the age of 65 say they use YouTube. Fully 95% of those 18 to 29 say they use the platform, along with 91% of those 30 to 49 and 83% of adults 50 to 64. However, this share drops substantially – to 49% – among those 65 and older.

By comparison, age gaps between the youngest and oldest Americans are narrower for Facebook. Fully 70% of those ages 18 to 29 say they use the platform, and those shares are statistically the same for those ages 30 to 49 (77%) or ages 50 to 64 (73%). Half of those 65 and older say they use the site – making Facebook and YouTube the two most used platforms among this older population.

Other sites and apps stand out for their demographic differences:

• Instagram: About half of Hispanic (52%) and Black Americans (49%) say they use the platform, compared with smaller shares of White Americans (35%) who say the same. 2
• WhatsApp: Hispanic Americans (46%) are far more likely to say they use WhatsApp than Black (23%) or White Americans (16%). Hispanics also stood out for their WhatsApp use in the Center’s previous surveys on this topic.
• LinkedIn: Those with higher levels of education are again more likely than those with lower levels of educational attainment to report being LinkedIn users. Roughly half of adults who have a bachelor’s or advanced degree (51%) say they use LinkedIn, compared with smaller shares of those with some college experience (28%) and those with a high school diploma or less (10%).
• Pinterest: Women continue to be far more likely than men to say they use Pinterest when compared with male counterparts, by a difference of 30 points (46% vs. 16%).
• Nextdoor: There are large differences in use of this platform by community type. Adults living in urban (17%) or suburban (14%) areas are more likely to say they use Nextdoor. Just 2% of rural Americans report using the site.

A majority of Facebook, Snapchat and Instagram users say they visit these platforms on a daily basis

While there has been much written about Americans’ changing relationship with Facebook , its users remain quite active on the platform. Seven-in-ten Facebook users say they use the site daily, including 49% who say they use the site several times a day. (These figures are statistically unchanged from those reported in the Center’s 2019 survey about social media use.)  

Smaller shares – though still a majority – of Snapchat or Instagram users report visiting these respective platforms daily (59% for both). And being active on these sites is especially common for younger users. For instance, 71% of Snapchat users ages 18 to 29 say they use the app daily, including six-in-ten who say they do this multiple times a day. The pattern is similar for Instagram: 73% of 18- to 29-year-old Instagram users say they visit the site every day, with roughly half (53%) reporting they do so several times per day.

YouTube is used daily by 54% if its users, with 36% saying they visit the site several times a day. By comparison, Twitter is used less frequently, with fewer than half of its users (46%) saying they visit the site daily.

• Due to a limited sample size, figures for those ages 25 to 29 cannot be reported on separately. ↩
• There were not enough Asian American respondents in the sample to be broken out into a separate analysis. As always, their responses are incorporated into the general population figures throughout this report. ↩

Sign up for our weekly newsletter

Fresh data delivery Saturday mornings

Sign up for The Briefing

Weekly updates on the world of news & information

• Social Media
• User Demographics

America’s News Influencers

• Social Media Fact Sheet

More Americans – especially young adults – are regularly getting news on TikTok

Many israelis say social media content about the israel-hamas war should be censored, whatsapp and facebook dominate the social media landscape in middle-income nations, most popular, report materials.

• 2021 Core Trends Survey

901 E St. NW, Suite 300 Washington, DC 20004 USA (+1) 202-419-4300 | Main (+1) 202-857-8562 | Fax (+1) 202-419-4372 |  Media Inquiries

Research Topics

• Email Newsletters

ABOUT PEW RESEARCH CENTER  Pew Research Center is a nonpartisan, nonadvocacy fact tank that informs the public about the issues, attitudes and trends shaping the world. It does not take policy positions. The Center conducts public opinion polling, demographic research, computational social science research and other data-driven research. Pew Research Center is a subsidiary of The Pew Charitable Trusts , its primary funder.

© 2024 Pew Research Center

SYSTEMATIC REVIEW article

Research trends in social media addiction and problematic social media use: a bibliometric analysis.

• 1 Sasin School of Management, Chulalongkorn University, Bangkok, Thailand
• 2 Business Administration Division, Mahidol University International College, Mahidol University, Nakhon Pathom, Thailand

Despite their increasing ubiquity in people's lives and incredible advantages in instantly interacting with others, social media's impact on subjective well-being is a source of concern worldwide and calls for up-to-date investigations of the role social media plays in mental health. Much research has discovered how habitual social media use may lead to addiction and negatively affect adolescents' school performance, social behavior, and interpersonal relationships. The present study was conducted to review the extant literature in the domain of social media and analyze global research productivity during 2013–2022. Bibliometric analysis was conducted on 501 articles that were extracted from the Scopus database using the keywords social media addiction and problematic social media use. The data were then uploaded to VOSviewer software to analyze citations, co-citations, and keyword co-occurrences. Volume, growth trajectory, geographic distribution of the literature, influential authors, intellectual structure of the literature, and the most prolific publishing sources were analyzed. The bibliometric analysis presented in this paper shows that the US, the UK, and Turkey accounted for 47% of the publications in this field. Most of the studies used quantitative methods in analyzing data and therefore aimed at testing relationships between variables. In addition, the findings in this study show that most analysis were cross-sectional. Studies were performed on undergraduate students between the ages of 19–25 on the use of two social media platforms: Facebook and Instagram. Limitations as well as research directions for future studies are also discussed.

Introduction

Social media generally refers to third-party internet-based platforms that mainly focus on social interactions, community-based inputs, and content sharing among its community of users and only feature content created by their users and not that licensed from third parties ( 1 ). Social networking sites such as Facebook, Instagram, and TikTok are prominent examples of social media that allow people to stay connected in an online world regardless of geographical distance or other obstacles ( 2 , 3 ). Recent evidence suggests that social networking sites have become increasingly popular among adolescents following the strict policies implemented by many countries to counter the COVID-19 pandemic, including social distancing, “lockdowns,” and quarantine measures ( 4 ). In this new context, social media have become an essential part of everyday life, especially for children and adolescents ( 5 ). For them such media are a means of socialization that connect people together. Interestingly, social media are not only used for social communication and entertainment purposes but also for sharing opinions, learning new things, building business networks, and initiate collaborative projects ( 6 ).

Among the 7.91 billion people in the world as of 2022, 4.62 billion active social media users, and the average time individuals spent using the internet was 6 h 58 min per day with an average use of social media platforms of 2 h and 27 min ( 7 ). Despite their increasing ubiquity in people's lives and the incredible advantages they offer to instantly interact with people, an increasing number of studies have linked social media use to negative mental health consequences, such as suicidality, loneliness, and anxiety ( 8 ). Numerous sources have expressed widespread concern about the effects of social media on mental health. A 2011 report by the American Academy of Pediatrics (AAP) identifies a phenomenon known as Facebook depression which may be triggered “when preteens and teens spend a great deal of time on social media sites, such as Facebook, and then begin to exhibit classic symptoms of depression” ( 9 ). Similarly, the UK's Royal Society for Public Health (RSPH) claims that there is a clear evidence of the relationship between social media use and mental health issues based on a survey of nearly 1,500 people between the ages of 14–24 ( 10 ). According to some authors, the increase in usage frequency of social media significantly increases the risks of clinical disorders described (and diagnosed) as “Facebook depression,” “fear of missing out” (FOMO), and “social comparison orientation” (SCO) ( 11 ). Other risks include sexting ( 12 ), social media stalking ( 13 ), cyber-bullying ( 14 ), privacy breaches ( 15 ), and improper use of technology. Therefore, social media's impact on subjective well-being is a source of concern worldwide and calls for up-to-date investigations of the role social media plays with regard to mental health ( 8 ). Many studies have found that habitual social media use may lead to addiction and thus negatively affect adolescents' school performance, social behavior, and interpersonal relationships ( 16 – 18 ). As a result of addiction, the user becomes highly engaged with online activities motivated by an uncontrollable desire to browse through social media pages and “devoting so much time and effort to it that it impairs other important life areas” ( 19 ).

Given these considerations, the present study was conducted to review the extant literature in the domain of social media and analyze global research productivity during 2013–2022. The study presents a bibliometric overview of the leading trends with particular regard to “social media addiction” and “problematic social media use.” This is valuable as it allows for a comprehensive overview of the current state of this field of research, as well as identifies any patterns or trends that may be present. Additionally, it provides information on the geographical distribution and prolific authors in this area, which may help to inform future research endeavors.

In terms of bibliometric analysis of social media addiction research, few studies have attempted to review the existing literature in the domain extensively. Most previous bibliometric studies on social media addiction and problematic use have focused mainly on one type of screen time activity such as digital gaming or texting ( 20 ) and have been conducted with a focus on a single platform such as Facebook, Instagram, or Snapchat ( 21 , 22 ). The present study adopts a more comprehensive approach by including all social media platforms and all types of screen time activities in its analysis.

Additionally, this review aims to highlight the major themes around which the research has evolved to date and draws some guidance for future research directions. In order to meet these objectives, this work is oriented toward answering the following research questions:

(1) What is the current status of research focusing on social media addiction?

(2) What are the key thematic areas in social media addiction and problematic use research?

(3) What is the intellectual structure of social media addiction as represented in the academic literature?

(4) What are the key findings of social media addiction and problematic social media research?

(5) What possible future research gaps can be identified in the field of social media addiction?

These research questions will be answered using bibliometric analysis of the literature on social media addiction and problematic use. This will allow for an overview of the research that has been conducted in this area, including information on the most influential authors, journals, countries of publication, and subject areas of study. Part 2 of the study will provide an examination of the intellectual structure of the extant literature in social media addiction while Part 3 will discuss the research methodology of the paper. Part 4 will discuss the findings of the study followed by a discussion under Part 5 of the paper. Finally, in Part 7, gaps in current knowledge about this field of research will be identified.

Literature review

Social media addiction research context.

Previous studies on behavioral addictions have looked at a lot of different factors that affect social media addiction focusing on personality traits. Although there is some inconsistency in the literature, numerous studies have focused on three main personality traits that may be associated with social media addiction, namely anxiety, depression, and extraversion ( 23 , 24 ).

It has been found that extraversion scores are strongly associated with increased use of social media and addiction to it ( 25 , 26 ). People with social anxiety as well as people who have psychiatric disorders often find online interactions extremely appealing ( 27 ). The available literature also reveals that the use of social media is positively associated with being female, single, and having attention deficit hyperactivity disorder (ADHD), obsessive compulsive disorder (OCD), or anxiety ( 28 ).

In a study by Seidman ( 29 ), the Big Five personality traits were assessed using Saucier's ( 30 ) Mini-Markers Scale. Results indicated that neurotic individuals use social media as a safe place for expressing their personality and meet belongingness needs. People affected by neurosis tend to use online social media to stay in touch with other people and feel better about their social lives ( 31 ). Narcissism is another factor that has been examined extensively when it comes to social media, and it has been found that people who are narcissistic are more likely to become addicted to social media ( 32 ). In this case users want to be seen and get “likes” from lots of other users. Longstreet and Brooks ( 33 ) did a study on how life satisfaction depends on how much money people make. Life satisfaction was found to be negatively linked to social media addiction, according to the results. When social media addiction decreases, the level of life satisfaction rises. But results show that in lieu of true-life satisfaction people use social media as a substitute (for temporary pleasure vs. longer term happiness).

Researchers have discovered similar patterns in students who tend to rank high in shyness: they find it easier to express themselves online rather than in person ( 34 , 35 ). With the use of social media, shy individuals have the opportunity to foster better quality relationships since many of their anxiety-related concerns (e.g., social avoidance and fear of social devaluation) are significantly reduced ( 36 , 37 ).

Problematic use of social media

The amount of research on problematic use of social media has dramatically increased since the last decade. But using social media in an unhealthy manner may not be considered an addiction or a disorder as this behavior has not yet been formally categorized as such ( 38 ). Although research has shown that people who use social media in a negative way often report negative health-related conditions, most of the data that have led to such results and conclusions comprise self-reported data ( 39 ). The dimensions of excessive social media usage are not exactly known because there are not enough diagnostic criteria and not enough high-quality long-term studies available yet. This is what Zendle and Bowden-Jones ( 40 ) noted in their own research. And this is why terms like “problematic social media use” have been used to describe people who use social media in a negative way. Furthermore, if a lot of time is spent on social media, it can be hard to figure out just when it is being used in a harmful way. For instance, people easily compare their appearance to what they see on social media, and this might lead to low self-esteem if they feel they do not look as good as the people they are following. According to research in this domain, the extent to which an individual engages in photo-related activities (e.g., taking selfies, editing photos, checking other people's photos) on social media is associated with negative body image concerns. Through curated online images of peers, adolescents face challenges to their self-esteem and sense of self-worth and are increasingly isolated from face-to-face interaction.

To address this problem the Diagnostic and Statistical Manual of Mental Disorders (DSM-V) has been used by some scholars ( 41 , 42 ). These scholars have used criteria from the DSM-V to describe one problematic social media use, internet gaming disorder, but such criteria could also be used to describe other types of social media disorders. Franchina et al. ( 43 ) and Scott and Woods ( 44 ), for example, focus their attention on individual-level factors (like fear of missing out) and family-level factors (like childhood abuse) that have been used to explain why people use social media in a harmful way. Friends-level factors have also been explored as a social well-being measurement to explain why people use social media in a malevolent way and demonstrated significant positive correlations with lower levels of friend support ( 45 ). Macro-level factors have also been suggested, such as the normalization of surveillance ( 46 ) and the ability to see what people are doing online ( 47 ). Gender and age seem to be highly associated to the ways people use social media negatively. Particularly among girls, social media use is consistently associated with mental health issues ( 41 , 48 , 49 ), an association more common among older girls than younger girls ( 46 , 48 ).

Most studies have looked at the connection between social media use and its effects (such as social media addiction) and a number of different psychosomatic disorders. In a recent study conducted by Vannucci and Ohannessian ( 50 ), the use of social media appears to have a variety of effects “on psychosocial adjustment during early adolescence, with high social media use being the most problematic.” It has been found that people who use social media in a harmful way are more likely to be depressed, anxious, have low self-esteem, be more socially isolated, have poorer sleep quality, and have more body image dissatisfaction. Furthermore, harmful social media use has been associated with unhealthy lifestyle patterns (for example, not getting enough exercise or having trouble managing daily obligations) as well as life threatening behaviors such as illicit drug use, excessive alcohol consumption and unsafe sexual practices ( 51 , 52 ).

A growing body of research investigating social media use has revealed that the extensive use of social media platforms is correlated with a reduced performance on cognitive tasks and in mental effort ( 53 ). Overall, it appears that individuals who have a problematic relationship with social media or those who use social media more frequently are more likely to develop negative health conditions.

Social media addiction and problematic use systematic reviews

Previous studies have revealed the detrimental impacts of social media addiction on users' health. A systematic review by Khan and Khan ( 20 ) has pointed out that social media addiction has a negative impact on users' mental health. For example, social media addiction can lead to stress levels rise, loneliness, and sadness ( 54 ). Anxiety is another common mental health problem associated with social media addiction. Studies have found that young adolescents who are addicted to social media are more likely to suffer from anxiety than people who are not addicted to social media ( 55 ). In addition, social media addiction can also lead to physical health problems, such as obesity and carpal tunnel syndrome a result of spending too much time on the computer ( 22 ).

Apart from the negative impacts of social media addiction on users' mental and physical health, social media addiction can also lead to other problems. For example, social media addiction can lead to financial problems. A study by Sharif and Yeoh ( 56 ) has found that people who are addicted to social media tend to spend more money than those who are not addicted to social media. In addition, social media addiction can also lead to a decline in academic performance. Students who are addicted to social media are more likely to have lower grades than those who are not addicted to social media ( 57 ).

Research methodology

Bibliometric analysis.

Merigo et al. ( 58 ) use bibliometric analysis to examine, organize, and analyze a large body of literature from a quantitative, objective perspective in order to assess patterns of research and emerging trends in a certain field. A bibliometric methodology is used to identify the current state of the academic literature, advance research. and find objective information ( 59 ). This technique allows the researchers to examine previous scientific work, comprehend advancements in prior knowledge, and identify future study opportunities.

To achieve this objective and identify the research trends in social media addiction and problematic social media use, this study employs two bibliometric methodologies: performance analysis and science mapping. Performance analysis uses a series of bibliometric indicators (e.g., number of annual publications, document type, source type, journal impact factor, languages, subject area, h-index, and countries) and aims at evaluating groups of scientific actors on a particular topic of research. VOSviewer software ( 60 ) was used to carry out the science mapping. The software is used to visualize a particular body of literature and map the bibliographic material using the co-occurrence analysis of author, index keywords, nations, and fields of publication ( 61 , 62 ).

Data collection

After picking keywords, designing the search strings, and building up a database, the authors conducted a bibliometric literature search. Scopus was utilized to gather exploration data since it is a widely used database that contains the most comprehensive view of the world's research output and provides one of the most effective search engines. If the research was to be performed using other database such as Web Of Science or Google Scholar the authors may have obtained larger number of articles however they may not have been all particularly relevant as Scopus is known to have the most widest and most relevant scholar search engine in marketing and social science. A keyword search for “social media addiction” OR “problematic social media use” yielded 553 papers, which were downloaded from Scopus. The information was gathered in March 2022, and because the Scopus database is updated on a regular basis, the results may change in the future. Next, the authors examined the titles and abstracts to see whether they were relevant to the topics treated. There were two common grounds for document exclusion. First, while several documents emphasized the negative effects of addiction in relation to the internet and digital media, they did not focus on social networking sites specifically. Similarly, addiction and problematic consumption habits were discussed in relation to social media in several studies, although only in broad terms. This left a total of 511 documents. Articles were then limited only to journal articles, conference papers, reviews, books, and only those published in English. This process excluded 10 additional documents. Then, the relevance of the remaining articles was finally checked by reading the titles, abstracts, and keywords. Documents were excluded if social networking sites were only mentioned as a background topic or very generally. This resulted in a final selection of 501 research papers, which were then subjected to bibliometric analysis (see Figure 1 ).

Figure 1 . Preferred reporting items for systematic reviews and meta-analysis (PRISMA) flowchart showing the search procedures used in the review.

After identifying 501 Scopus files, bibliographic data related to these documents were imported into an Excel sheet where the authors' names, their affiliations, document titles, keywords, abstracts, and citation figures were analyzed. These were subsequently uploaded into VOSViewer software version 1.6.8 to begin the bibliometric review. Descriptive statistics were created to define the whole body of knowledge about social media addiction and problematic social media use. VOSViewer was used to analyze citation, co-citation, and keyword co-occurrences. According to Zupic and Cater ( 63 ), co-citation analysis measures the influence of documents, authors, and journals heavily cited and thus considered influential. Co-citation analysis has the objective of building similarities between authors, journals, and documents and is generally defined as the frequency with which two units are cited together within the reference list of a third article.

The implementation of social media addiction performance analysis was conducted according to the models recently introduced by Karjalainen et al. ( 64 ) and Pattnaik ( 65 ). Throughout the manuscript there are operational definitions of relevant terms and indicators following a standardized bibliometric approach. The cumulative academic impact (CAI) of the documents was measured by the number of times they have been cited in other scholarly works while the fine-grained academic impact (FIA) was computed according to the authors citation analysis and authors co-citation analysis within the reference lists of documents that have been specifically focused on social media addiction and problematic social media use.

Results of the study presented here include the findings on social media addiction and social media problematic use. The results are presented by the foci outlined in the study questions.

Volume, growth trajectory, and geographic distribution of the literature

After performing the Scopus-based investigation of the current literature regarding social media addiction and problematic use of social media, the authors obtained a knowledge base consisting of 501 documents comprising 455 journal articles, 27 conference papers, 15 articles reviews, 3 books and 1 conference review. The included literature was very recent. As shown in Figure 2 , publication rates started very slowly in 2013 but really took off in 2018, after which publications dramatically increased each year until a peak was reached in 2021 with 195 publications. Analyzing the literature published during the past decade reveals an exponential increase in scholarly production on social addiction and its problematic use. This might be due to the increasingly widespread introduction of social media sites in everyday life and the ubiquitous diffusion of mobile devices that have fundamentally impacted human behavior. The dip in the number of publications in 2022 is explained by the fact that by the time the review was carried out the year was not finished yet and therefore there are many articles still in press.

Figure 2 . Annual volume of social media addiction or social media problematic use ( n = 501).

The geographical distribution trends of scholarly publications on social media addiction or problematic use of social media are highlighted in Figure 3 . The articles were assigned to a certain country according to the nationality of the university with whom the first author was affiliated with. The figure shows that the most productive countries are the USA (92), the U.K. (79), and Turkey ( 63 ), which combined produced 236 articles, equal to 47% of the entire scholarly production examined in this bibliometric analysis. Turkey has slowly evolved in various ways with the growth of the internet and social media. Anglo-American scholarly publications on problematic social media consumer behavior represent the largest research output. Yet it is interesting to observe that social networking sites studies are attracting many researchers in Asian countries, particularly China. For many Chinese people, social networking sites are a valuable opportunity to involve people in political activism in addition to simply making purchases ( 66 ).

Figure 3 . Global dispersion of social networking sites in relation to social media addiction or social media problematic use.

Analysis of influential authors

This section analyses the high-impact authors in the Scopus-indexed knowledge base on social networking sites in relation to social media addiction or problematic use of social media. It provides valuable insights for establishing patterns of knowledge generation and dissemination of literature about social networking sites relating to addiction and problematic use.

Table 1 acknowledges the top 10 most highly cited authors with the highest total citations in the database.

Table 1 . Highly cited authors on social media addiction and problematic use ( n = 501).

Table 1 shows that MD Griffiths (sixty-five articles), CY Lin (twenty articles), and AH Pakpour (eighteen articles) are the most productive scholars according to the number of Scopus documents examined in the area of social media addiction and its problematic use . If the criteria are changed and authors ranked according to the overall number of citations received in order to determine high-impact authors, the same three authors turn out to be the most highly cited authors. It should be noted that these highly cited authors tend to enlist several disciplines in examining social media addiction and problematic use. Griffiths, for example, focuses on behavioral addiction stemming from not only digital media usage but also from gambling and video games. Lin, on the other hand, focuses on the negative effects that the internet and digital media can have on users' mental health, and Pakpour approaches the issue from a behavioral medicine perspective.

Intellectual structure of the literature

In this part of the paper, the authors illustrate the “intellectual structure” of the social media addiction and the problematic use of social media's literature. An author co-citation analysis (ACA) was performed which is displayed as a figure that depicts the relations between highly co-cited authors. The study of co-citation assumes that strongly co-cited authors carry some form of intellectual similarity ( 67 ). Figure 4 shows the author co-citation map. Nodes represent units of analysis (in this case scholars) and network ties represent similarity connections. Nodes are sized according to the number of co-citations received—the bigger the node, the more co-citations it has. Adjacent nodes are considered intellectually similar.

Figure 4 . Two clusters, representing the intellectual structure of the social media and its problematic use literature.

Scholars belonging to the green cluster (Mental Health and Digital Media Addiction) have extensively published on medical analysis tools and how these can be used to heal users suffering from addiction to digital media, which can range from gambling, to internet, to videogame addictions. Scholars in this school of thought focus on the negative effects on users' mental health, such as depression, anxiety, and personality disturbances. Such studies focus also on the role of screen use in the development of mental health problems and the increasing use of medical treatments to address addiction to digital media. They argue that addiction to digital media should be considered a mental health disorder and treatment options should be made available to users.

In contrast, scholars within the red cluster (Social Media Effects on Well Being and Cyberpsychology) have focused their attention on the effects of social media toward users' well-being and how social media change users' behavior, focusing particular attention on the human-machine interaction and how methods and models can help protect users' well-being. Two hundred and two authors belong to this group, the top co-cited being Andreassen (667 co-citations), Pallasen (555 co-citations), and Valkenburg (215 co-citations). These authors have extensively studied the development of addiction to social media, problem gambling, and internet addiction. They have also focused on the measurement of addiction to social media, cyberbullying, and the dark side of social media.

Most influential source title in the field of social media addiction and its problematic use

To find the preferred periodicals in the field of social media addiction and its problematic use, the authors have selected 501 articles published in 263 journals. Table 2 gives a ranked list of the top 10 journals that constitute the core publishing sources in the field of social media addiction research. In doing so, the authors analyzed the journal's impact factor, Scopus Cite Score, h-index, quartile ranking, and number of publications per year.

Table 2 . Top 10 most cited and more frequently mentioned documents in the field of social media addiction.

The journal Addictive Behaviors topped the list, with 700 citations and 22 publications (4.3%), followed by Computers in Human Behaviors , with 577 citations and 13 publications (2.5%), Journal of Behavioral Addictions , with 562 citations and 17 publications (3.3%), and International Journal of Mental Health and Addiction , with 502 citations and 26 publications (5.1%). Five of the 10 most productive journals in the field of social media addiction research are published by Elsevier (all Q1 rankings) while Springer and Frontiers Media published one journal each.

Documents citation analysis identified the most influential and most frequently mentioned documents in a certain scientific field. Andreassen has received the most citations among the 10 most significant papers on social media addiction, with 405 ( Table 2 ). The main objective of this type of studies was to identify the associations and the roles of different variables as predictors of social media addiction (e.g., ( 19 , 68 , 69 )). According to general addiction models, the excessive and problematic use of digital technologies is described as “being overly concerned about social media, driven by an uncontrollable motivation to log on to or use social media, and devoting so much time and effort to social media that it impairs other important life areas” ( 27 , 70 ). Furthermore, the purpose of several highly cited studies ( 31 , 71 ) was to analyse the connections between young adults' sleep quality and psychological discomfort, depression, self-esteem, and life satisfaction and the severity of internet and problematic social media use, since the health of younger generations and teenagers is of great interest this may help explain the popularity of such papers. Despite being the most recent publication Lin et al.'s work garnered more citations annually. The desire to quantify social media addiction in individuals can also help explain the popularity of studies which try to develop measurement scales ( 42 , 72 ). Some of the highest-ranked publications are devoted to either the presentation of case studies or testing relationships among psychological constructs ( 73 ).

Keyword co-occurrence analysis

The research question, “What are the key thematic areas in social media addiction literature?” was answered using keyword co-occurrence analysis. Keyword co-occurrence analysis is conducted to identify research themes and discover keywords. It mainly examines the relationships between co-occurrence keywords in a wide variety of literature ( 74 ). In this approach, the idea is to explore the frequency of specific keywords being mentioned together.

Utilizing VOSviewer, the authors conducted a keyword co-occurrence analysis to characterize and review the developing trends in the field of social media addiction. The top 10 most frequent keywords are presented in Table 3 . The results indicate that “social media addiction” is the most frequent keyword (178 occurrences), followed by “problematic social media use” (74 occurrences), “internet addiction” (51 occurrences), and “depression” (46 occurrences). As shown in the co-occurrence network ( Figure 5 ), the keywords can be grouped into two major clusters. “Problematic social media use” can be identified as the core theme of the green cluster. In the red cluster, keywords mainly identify a specific aspect of problematic social media use: social media addiction.

Table 3 . Frequency of occurrence of top 10 keywords.

Figure 5 . Keywords co-occurrence map. Threshold: 5 co-occurrences.

The results of the keyword co-occurrence analysis for journal articles provide valuable perspectives and tools for understanding concepts discussed in past studies of social media usage ( 75 ). More precisely, it can be noted that there has been a large body of research on social media addiction together with other types of technological addictions, such as compulsive web surfing, internet gaming disorder, video game addiction and compulsive online shopping ( 76 – 78 ). This field of research has mainly been directed toward teenagers, middle school students, and college students and university students in order to understand the relationship between social media addiction and mental health issues such as depression, disruptions in self-perceptions, impairment of social and emotional activity, anxiety, neuroticism, and stress ( 79 – 81 ).

The findings presented in this paper show that there has been an exponential increase in scholarly publications—from two publications in 2013 to 195 publications in 2021. There were 45 publications in 2022 at the time this study was conducted. It was interesting to observe that the US, the UK, and Turkey accounted for 47% of the publications in this field even though none of these countries are in the top 15 countries in terms of active social media penetration ( 82 ) although the US has the third highest number of social media users ( 83 ). Even though China and India have the highest number of social media users ( 83 ), first and second respectively, they rank fifth and tenth in terms of publications on social media addiction or problematic use of social media. In fact, the US has almost double the number of publications in this field compared to China and almost five times compared to India. Even though East Asia, Southeast Asia, and South Asia make up the top three regions in terms of worldwide social media users ( 84 ), except for China and India there have been only a limited number of publications on social media addiction or problematic use. An explanation for that could be that there is still a lack of awareness on the negative consequences of the use of social media and the impact it has on the mental well-being of users. More research in these regions should perhaps be conducted in order to understand the problematic use and addiction of social media so preventive measures can be undertaken.

From the bibliometric analysis, it was found that most of the studies examined used quantitative methods in analyzing data and therefore aimed at testing relationships between variables. In addition, many studies were empirical, aimed at testing relationships based on direct or indirect observations of social media use. Very few studies used theories and for the most part if they did they used the technology acceptance model and social comparison theories. The findings presented in this paper show that none of the studies attempted to create or test new theories in this field, perhaps due to the lack of maturity of the literature. Moreover, neither have very many qualitative studies been conducted in this field. More qualitative research in this field should perhaps be conducted as it could explore the motivations and rationales from which certain users' behavior may arise.

The authors found that almost all the publications on social media addiction or problematic use relied on samples of undergraduate students between the ages of 19–25. The average daily time spent by users worldwide on social media applications was highest for users between the ages of 40–44, at 59.85 min per day, followed by those between the ages of 35–39, at 59.28 min per day, and those between the ages of 45–49, at 59.23 per day ( 85 ). Therefore, more studies should be conducted exploring different age groups, as users between the ages of 19–25 do not represent the entire population of social media users. Conducting studies on different age groups may yield interesting and valuable insights to the field of social media addiction. For example, it would be interesting to measure the impacts of social media use among older users aged 50 years or older who spend almost the same amount of time on social media as other groups of users (56.43 min per day) ( 85 ).

A majority of the studies tested social media addiction or problematic use based on only two social media platforms: Facebook and Instagram. Although Facebook and Instagram are ranked first and fourth in terms of most popular social networks by number of monthly users, it would be interesting to study other platforms such as YouTube, which is ranked second, and WhatsApp, which is ranked third ( 86 ). Furthermore, TikTok would also be an interesting platform to study as it has grown in popularity in recent years, evident from it being the most downloaded application in 2021, with 656 million downloads ( 87 ), and is ranked second in Q1 of 2022 ( 88 ). Moreover, most of the studies focused only on one social media platform. Comparing different social media platforms would yield interesting results because each platform is different in terms of features, algorithms, as well as recommendation engines. The purpose as well as the user behavior for using each platform is also different, therefore why users are addicted to these platforms could provide a meaningful insight into social media addiction and problematic social media use.

Lastly, most studies were cross-sectional, and not longitudinal, aiming at describing results over a certain point in time and not over a long period of time. A longitudinal study could better describe the long-term effects of social media use.

This study was conducted to review the extant literature in the field of social media and analyze the global research productivity during the period ranging from 2013 to 2022. The study presents a bibliometric overview of the leading trends with particular regard to “social media addiction” and “problematic social media use.” The authors applied science mapping to lay out a knowledge base on social media addiction and its problematic use. This represents the first large-scale analysis in this area of study.

A keyword search of “social media addiction” OR “problematic social media use” yielded 553 papers, which were downloaded from Scopus. After performing the Scopus-based investigation of the current literature regarding social media addiction and problematic use, the authors ended up with a knowledge base consisting of 501 documents comprising 455 journal articles, 27 conference papers, 15 articles reviews, 3 books, and 1 conference review.

The geographical distribution trends of scholarly publications on social media addiction or problematic use indicate that the most productive countries were the USA (92), the U.K. (79), and Turkey ( 63 ), which together produced 236 articles. Griffiths (sixty-five articles), Lin (twenty articles), and Pakpour (eighteen articles) were the most productive scholars according to the number of Scopus documents examined in the area of social media addiction and its problematic use. An author co-citation analysis (ACA) was conducted which generated a layout of social media effects on well-being and cyber psychology as well as mental health and digital media addiction in the form of two research literature clusters representing the intellectual structure of social media and its problematic use.

The preferred periodicals in the field of social media addiction and its problematic use were Addictive Behaviors , with 700 citations and 22 publications, followed by Computers in Human Behavior , with 577 citations and 13 publications, and Journal of Behavioral Addictions , with 562 citations and 17 publications. Keyword co-occurrence analysis was used to investigate the key thematic areas in the social media literature, as represented by the top three keyword phrases in terms of their frequency of occurrence, namely, “social media addiction,” “problematic social media use,” and “social media addiction.”

This research has a few limitations. The authors used science mapping to improve the comprehension of the literature base in this review. First and foremost, the authors want to emphasize that science mapping should not be utilized in place of established review procedures, but rather as a supplement. As a result, this review can be considered the initial stage, followed by substantive research syntheses that examine findings from recent research. Another constraint stems from how 'social media addiction' is defined. The authors overcame this limitation by inserting the phrase “social media addiction” OR “problematic social media use” in the search string. The exclusive focus on SCOPUS-indexed papers creates a third constraint. The SCOPUS database has a larger number of papers than does Web of Science although it does not contain all the publications in a given field.

Although the total body of literature on social media addiction is larger than what is covered in this review, the use of co-citation analyses helped to mitigate this limitation. This form of bibliometric study looks at all the publications listed in the reference list of the extracted SCOPUS database documents. As a result, a far larger dataset than the one extracted from SCOPUS initially has been analyzed.

The interpretation of co-citation maps should be mentioned as a last constraint. The reason is that the procedure is not always clear, so scholars must have a thorough comprehension of the knowledge base in order to make sense of the result of the analysis ( 63 ). This issue was addressed by the authors' expertise, but it remains somewhat subjective.

Implications

The findings of this study have implications mainly for government entities and parents. The need for regulation of social media addiction is evident when considering the various risks associated with habitual social media use. Social media addiction may lead to negative consequences for adolescents' school performance, social behavior, and interpersonal relationships. In addition, social media addiction may also lead to other risks such as sexting, social media stalking, cyber-bullying, privacy breaches, and improper use of technology. Given the seriousness of these risks, it is important to have regulations in place to protect adolescents from the harms of social media addiction.

Regulation of social media platforms

One way that regulation could help protect adolescents from the harms of social media addiction is by limiting their access to certain websites or platforms. For example, governments could restrict adolescents' access to certain websites or platforms during specific hours of the day. This would help ensure that they are not spending too much time on social media and are instead focusing on their schoolwork or other important activities.

Another way that regulation could help protect adolescents from the harms of social media addiction is by requiring companies to put warning labels on their websites or apps. These labels would warn adolescents about the potential risks associated with excessive use of social media.

Finally, regulation could also require companies to provide information about how much time each day is recommended for using their website or app. This would help adolescents make informed decisions about how much time they want to spend on social media each day. These proposed regulations would help to protect children from the dangers of social media, while also ensuring that social media companies are more transparent and accountable to their users.

Parental involvement in adolescents' social media use

Parents should be involved in their children's social media use to ensure that they are using these platforms safely and responsibly. Parents can monitor their children's online activity, set time limits for social media use, and talk to their children about the risks associated with social media addiction.

Education on responsible social media use

Adolescents need to be educated about responsible social media use so that they can enjoy the benefits of these platforms while avoiding the risks associated with addiction. Education on responsible social media use could include topics such as cyber-bullying, sexting, and privacy breaches.

Research directions for future studies

A content analysis was conducted to answer the fifth research questions “What are the potential research directions for addressing social media addiction in the future?” The study reveals that there is a lack of screening instruments and diagnostic criteria to assess social media addiction. Validated DSM-V-based instruments could shed light on the factors behind social media use disorder. Diagnostic research may be useful in order to understand social media behavioral addiction and gain deeper insights into the factors responsible for psychological stress and psychiatric disorders. In addition to cross-sectional studies, researchers should also conduct longitudinal studies and experiments to assess changes in users' behavior over time ( 20 ).

Another important area to examine is the role of engagement-based ranking and recommendation algorithms in online habit formation. More research is required to ascertain how algorithms determine which content type generates higher user engagement. A clear understanding of the way social media platforms gather content from users and amplify their preferences would lead to the development of a standardized conceptualization of social media usage patterns ( 89 ). This may provide a clearer picture of the factors that lead to problematic social media use and addiction. It has been noted that “misinformation, toxicity, and violent content are inordinately prevalent” in material reshared by users and promoted by social media algorithms ( 90 ).

Additionally, an understanding of engagement-based ranking models and recommendation algorithms is essential in order to implement appropriate public policy measures. To address the specific behavioral concerns created by social media, legislatures must craft appropriate statutes. Thus, future qualitative research to assess engagement based ranking frameworks is extremely necessary in order to provide a broader perspective on social media use and tackle key regulatory gaps. Particular emphasis must be placed on consumer awareness, algorithm bias, privacy issues, ethical platform design, and extraction and monetization of personal data ( 91 ).

From a geographical perspective, the authors have identified some main gaps in the existing knowledge base that uncover the need for further research in certain regions of the world. Accordingly, the authors suggest encouraging more studies on internet and social media addiction in underrepresented regions with high social media penetration rates such as Southeast Asia and South America. In order to draw more contributions from these countries, journals with high impact factors could also make specific calls. This would contribute to educating social media users about platform usage and implement policy changes that support the development of healthy social media practices.

The authors hope that the findings gathered here will serve to fuel interest in this topic and encourage other scholars to investigate social media addiction in other contexts on newer platforms and among wide ranges of sample populations. In light of the rising numbers of people experiencing mental health problems (e.g., depression, anxiety, food disorders, and substance addiction) in recent years, it is likely that the number of papers related to social media addiction and the range of countries covered will rise even further.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.

Author contributions

AP took care of bibliometric analysis and drafting the paper. VB took care of proofreading and adding value to the paper. AS took care of the interpretation of the findings. All authors contributed to the article and approved the submitted version.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

1. Asur S and Huberman BA. “Predicting the future with social media,” in 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, Vol. 1 . IEEE (2010). p. 492–9. doi: 10.1109/WI-IAT.2010.63

CrossRef Full Text | Google Scholar

2. Kaye LK. Exploring the “socialness” of social media. Comput. Human Behav. Rep. (2021) 3:2021. doi: 10.1016/j.chbr.2021.100083

3. Boyd DM and Ellison NB. Social network sites: definition, history, and scholarship. J Computer-Med Commun. (2007) 13:210–30. doi: 10.1111/j.1083-6101.2007.00393.x

4. Marengo D, Fabris MA, Longobardi C, Settanni M. Smartphone and social media use contributed to individual tendencies towards social media addiction in Italian adolescents during the COVID-19 pandemic. Addictive Behav. (2022) 126:107204. doi: 10.1016/j.addbeh.2021.107204

PubMed Abstract | CrossRef Full Text | Google Scholar

5. Alshamrani S, Abusnaina A, Abuhamad M, Nyang D, Mohaisen D. “Hate, obscenity, and insults: Measuring the exposure of children to inappropriate comments in youtube,” in Companion Proceedings of the Web Conference . (2021). p. 508–515. doi: 10.1145/3442442.3452314

6. Malesev S, Cherry M. Digital and social media marketing-growing market share for construction SMEs. Construction Econom. Build. (2021) 21:65–82. doi: 10.5130/AJCEB.v21i1.7521

7. We Are Social. Digital 2022: Global Overview Report. (2022). Available online at: https://datareportal.com/reports/digital-2022-global-overview-report (accessed July 18, 2022).

Google Scholar

8. Latikka R, Koivula A, Oksa R, Savela N, Oksanen A. Loneliness and psychological distress before and during the COVID-19 pandemic: relationships with social media identity bubbles. Soc Sci Med. (2022) 293:114674. doi: 10.1016/j.socscimed.2021.114674

9. American Academy of Pediatrics. Supporting the health care transition from adolescence to adulthood in the medical home. Pediatrics. (2011) 128:182–200. doi: 10.1542/peds.2018-2587

10. Royal Society for Public Health. Social media addiction: The role of content. J Behav Addict. (2017) 9:64–77. doi: 10.1556/2006.6.2017.058

11. Blease CR. Too many “friends”, too few “likes”? Evolutionary psychology and Facebook depression. Rev. General Psychol. (2015) 19:1–13. doi: 10.1037/gpr0000030

12. Hasinoff AA. Sexting as media production: rethinking social media and sexuality. New Media Soc. (2013) 15:449–65. doi: 10.1177/1461444812459171

13. Sapone L. Moving fast and breaking things: an analysis of social media's revolutionary effects on culture and its impending regulation. Duq L Rev. (2021) 59:362.

14. Whittaker E, Kowalski RM. Cyberbullying via social media. J School Violence. (2015) 14:11–29. doi: 10.1080/15388220.2014.949377

15. Saravanakumar K, Deepa K. On privacy and security in social media–a comprehensive study. Procedia Computer Sci. (2016) 78:114–9. doi: 10.1016/j.procs.2016.02.019

16. Kalika M. Social networking continuance: When habit leads to information overload. Inf Process Manag. (2015) 5:10–31. doi: 10.18151/7217377

17. Kelly Y, Zilanawala A, Booker C, Sacker A. Social media use and adolescent mental health: findings from the UK Millennium cohort study. EClinicalMedicine. (2018) 6:59–68. doi: 10.1016/j.eclinm.2018.12.005

18. Twenge JM, Campbell WK. Associations between screen time and lower psychological well-being among children and adolescents: evidence from a population-based study. Prevent Med Rep. (2018) 12:271–83. doi: 10.1016/j.pmedr.2018.10.003

19. Andreassen CS, Pallesen S, Griffiths MD. The relationship between addictive use of social media, narcissism, and self-esteem: Findings from a large national survey. Addict Behav. (2014) 64:287–93. doi: 10.1016/j.addbeh.2016.03.006

20. Khan NF, Khan MN. A bibliometric analysis of peer-reviewed literature on smartphone addiction and future research agenda. Asia-Pacific J Bus Admin. (2021) 14:199–201. doi: 10.1108/APJBA-09-2021-0430

21. Baran KS, Ghaffari H. The manifold research fields of Facebook: a bibliometric analysis. J Information Sci Theory Pract. (2017) 5:33–47. doi: 10.1633/JISTaP.2017.5.2.3

22. Rejeb A, Rejeb K, Abdollahi A, Treiblmaier H. The big picture on Instagram research: Insights from a bibliometric analysis. Telematics Informat. (2022) 25:101876. doi: 10.1016/j.tele.2022.101876

23. D'Arienzo MC, Boursier V, Griffiths MD. Addiction to social media and attachment styles: A systematic literature review. Int J Ment Health Addict. (2019) 17:1094–118. doi: 10.1007/s11469-019-00082-5

24. Kırcaburun K, Griffiths MD. Problematic Instagram use: The role of perceived feeling of presence and escapism. Int J Ment Health Addict. (2019) 17:909–21. doi: 10.1007/s11469-018-9895-7

25. Ho SS, Lwin MO, Lee EW. Till logout do us part? Comparison of factors predicting excessive social network sites use and addiction between Singaporean adolescents and adults. Comput. Human Behav. (2017) 75:632–42. doi: 10.1016/j.chb.2017.06.002

26. Kuss DJ, Griffiths MD. Online social networking and addiction—a review of the psychological literature. Int J Environ Res Public Health. (2011) 8:3528–52. doi: 10.3390/ijerph8093528

27. Buote VM, Wood E, Pratt M. Exploring similarities and differences between online and offline friendships: The role of attachment style. Comput Human Behav. (2009) 25:560–7. doi: 10.1016/j.chb.2008.12.022

28. Andreassen CS, Billieux J, Griffiths MD, Kuss DJ, Demetrovics Z, Mazzoni E, et al. The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: A large-scale cross-sectional study. Psychol Addict Behav. (2016) 30:252. doi: 10.1037/adb0000160

29. Seidman G. Self-presentation and belonging on Facebook: How personality influences social media use and motivations. Pers Individ Dif. (2013) 54:402–7. doi: 10.1016/j.paid.2012.10.009

30. Saucier G. Mini-Markers: A brief version of Goldberg's unipolar Big-Five markers. J Personal Assess. (1994) 63:506–16. doi: 10.1207/s15327752jpa6303_8

31. Blackwell D, Leaman C, Tramposch R, Osborne C, Liss M. Extraversion, neuroticism, attachment style and fear of missing out as predictors of social media use and addiction. Personal Individ Diff. (2017) 116:69–72. doi: 10.1016/j.paid.2017.04.039

32. Hawk ST, van den Eijnden RJ, van Lissa CJ, ter Bogt TF. Narcissistic adolescents' attention-seeking following social rejection: Links with social media disclosure, problematic social media use, and smartphone stress. Comput Human Behav. (2019) 92:65–75. doi: 10.1016/j.chb.2018.10.032

33. Longstreet P, Brooks S. Life satisfaction: a key to managing internet and social media addiction. Technol Soc. (2017) 50:73–7. doi: 10.1016/j.techsoc.2017.05.003

34. Marriott TC, Buchanan T. The true self online: personality correlates of preference for self-expression online, and observer ratings of personality online and offline. Comput Human Behav. (2014) 32:171–7. doi: 10.1016/j.chb.2013.11.014

35. Orr ES, Sisic M, Ross C, Simmering MG, Arseneault JM, Orr RR. The influence of shyness on the use of Facebook in an undergraduate sample. Cyberpsychol Behav. (2009) 12:337–40. doi: 10.1089/cpb.2008.0214

36. Brunet PM, Schmidt LA. Is shyness context specific? Relation between shyness and online self-disclosure with and without a live webcam in young adults. J Res Pers. (2007) 41:938–45.

37. Roberts SG, Wilson R, Fedurek P, Dunbar RI. Individual differences and personal social network size and structure. Pers Individ Dif. (2000) 44:954–64. doi: 10.1016/j.jrp.2006.09.001

38. Wegmann E, Müller SM, Turel O, Brand M. Interactions of impulsivity, general executive functions, and specific inhibitory control explain symptoms of social-networks-use disorder: an experimental study. Sci Rep. (2020) 10:3866. doi: 10.1038/s41598-020-60819-4

39. Motoki K, Suzuki S, Kawashima R, Sugiura M. A combination of self-reported data and social-related neural measures forecasts viral marketing success on social media. J Interactive Market. (2020) 52:99–117. doi: 10.1016/j.intmar.2020.06.003

40. Zendle D, Bowden-Jones H. Is excessive use of social media an addiction? BMJ . (2019) 365:2171. doi: 10.1136/bmj.l2171

41. Bányai F, Zsila Á, Király O, Maraz A, Elekes Z, Griffiths MD, et al. Problematic social media use: results from a large-scale nationally representative adolescent sample. PLoS ONE. (2017) 12:e0169839. doi: 10.1371/journal.pone.0169839

42. Van den Eijnden RJ, Lemmens JS, Valkenburg PM. The social media disorder scale. Comput Human Behav. (2016) 61:478–87. doi: 10.1016/j.chb.2016.03.038

43. Franchina V, Vanden Abeele M, Van Rooij AJ, Lo Coco G, De Marez L. Fear of missing out as a predictor of problematic social media use and phubbing behavior among Flemish adolescents. Int J Environ Res Public Health. (2018) 15:2319. doi: 10.3390/ijerph15102319

44. Scott H, Woods HC. Fear of missing out and sleep: Cognitive behavioural factors in adolescents' nighttime social media use. J Adolescence. (2018) 68:61–5. doi: 10.1016/j.adolescence.2018.07.009

45. Boer M, Van Den Eijnden RJ, Boniel-Nissim M, Wong SL, Inchley JC, Badura P, et al. Adolescents' intense and problematic social media use and their well-being in 29 countries. J Adolescent Health. (2020) 66:S89–99. doi: 10.1016/j.jadohealth.2020.02.014

46. Paakkari L, Tynjälä J, Lahti H, Ojala K, Lyyra N. Problematic social media use and health among adolescents. Int J Environ Res Public Health. (2021) 18:1885. doi: 10.3390/ijerph18041885

47. Lee EW, Ho SS, Lwin MO. Explicating problematic social network sites use: a review of concepts, theoretical frameworks, and future directions for communication theorizing. N Media Soc. (2017) 19:308–26. doi: 10.1177/1461444816671891

48. Inchley JC, Stevens GW, Samdal O, Currie DB. Enhancing understanding of adolescent health and well-being: the health behaviour in school-aged children study. J Adolescent Health. (2020) 66:S3–5. doi: 10.1016/j.jadohealth.2020.03.014

49. Buda G, Lukoševičiut, e J, Šalčiunaite L, Šmigelskas K. Possible effects of social media use on adolescent health behaviors and perceptions. Psychol Rep. (2021) 124:1031–48. doi: 10.1177/0033294120922481

50. Vannucci A, McCauley Ohannessian C. Social media use subgroups differentially predict psychosocial well-being during early adolescence. J Youth Adolescence. (2019) 48:1469–93. doi: 10.1007/s10964-019-01060-9

51. Lewycka S, Clark T, Peiris-John R, Fenaughty J, Bullen P, Denny S, et al. Downwards trends in adolescent risk-taking behaviours in New Zealand: Exploring driving forces for change. J Paediatr Child Health. (2018) 54:602–8. doi: 10.1111/jpc.13930

52. Vannucci A, Simpson EG, Gagnon S, Ohannessian CM. Social media use and risky behaviors in adolescents: a meta-analysis. J Adolescence. (2020) 79:258–74. doi: 10.1016/j.adolescence.2020.01.014

53. Uncapher MR, Lin L, Rosen LD, Kirkorian HL, Baron NS, Bailey K, et al. Media multitasking and cognitive, psychological, neural, learning differences. Pediatrics . (2017) 140(Suppl 2):S62–6. doi: 10.1542/peds.2016-1758D

54. Ali I, Balta M, Papadopoulos T. Social media platforms and social enterprise: bibliometric analysis and systematic review. Int J Inform Manage. (2022) 10:510. doi: 10.1016/j.ijinfomgt.2022.102510

55. Aparicio-Martinez P, Perea-Moreno AJ, Martinez-Jimenez MP, Redel-Macías MD, Vaquero-Abellan M, Pagliari C. A bibliometric analysis of the health field regarding social networks and young people. Int J Environ Res Public Health. (2019) 16:4024. doi: 10.3390/ijerph16204024

56. Sharif SP, Yeoh KK. Excessive Social Networking Sites Use and Online Compulsive Buying in Young Adults: the Mediating Role of Money Attitude. Young Consumers (2018).

57. Moreno-Guerrero AJ, Gómez-García G, López-Belmonte J, Rodríguez-Jiménez C. Internet addiction in the web of science database: a review of the literature with scientific mapping. Int J Environ Res Public Health. (2020) 17:27–53. doi: 10.3390/ijerph17082753

58. Merig, ó JM, Mas-Tur A, Roig-Tierno N, Ribeiro-Soriano D. A bibliometric overview of the Journal of Business Research between 1973 and 2014. J Business Res. (2015) 68:2645–53. doi: 10.1016/j.jbusres.2015.04.006

59. Kumar S, Pandey N, Burton B, Sureka R. Research patterns and intellectual structure of managerial auditing journal: a retrospective using bibliometric analysis during 1986–2019. Managerial Auditing J . (2021) 36:280–313. doi: 10.1108/MAJ-12-2019-2517

60. Van Eck NJ, Waltman L. CitNetExplorer: a new software tool for analyzing and visualizing citation networks. J Informetr. (2014) 8:802–23. doi: 10.1016/j.joi.2014.07.006

61. Durieux V, Gevenois PA. Bibliometric indicators: quality measurements of scientific publication. Radiology. (2010) 255:342–51. doi: 10.1148/radiol.09090626

62. Small H. Visualizing science by citation mapping. J Am Soc Information Sci. (1999) 50:799–813. doi: 10.1002/(SICI)1097-4571(1999)50:9 < 799::AID-ASI9>3.0.CO;2-G

63. Zupic I, Cater T. Bibliometric methods in management and organization. Organiz Res Methods. (2015) 18:429–72. doi: 10.1177/1094428114562629

64. Karjalainen J, Valtakoski A, KauraNen I. (2019). Interfirm network structure and firm resources: Towards a unifying concept. In: A Network Approach in Strategic Management: Emerging Trends and Research Concepts , Vol. 17. Fundacja Cognitione (2019). p. 227. doi: 10.7341/20211737

65. Pattnaik D. A bibliometric retrospection of marketing from the lens of psychology: Insights from psychology & marketing. Psychol Market. (2020) 38:834–65. doi: 10.1002/mar.21472

66. Chu SC, Chen HT, Gan C. Consumers' engagement with corporate social responsibility (CSR) communication in social media: evidence from China and the United States. J Business Res. (2020) 110:260–71. doi: 10.1016/j.jbusres.2020.01.036

67. Bush VD, Gilbert FW. The web as a medium: an exploratory comparison of internet users versus newspaper readers. J Market Theory Pract. (2002) 10:1–10. doi: 10.1080/10696679.2002.11501905

68. Griffiths MD, Kuss DJ, Demetrovics Z. Social networking addiction: An overview of preliminary findings. Behav Addict. (2014) 2014:119–41. doi: 10.1016/B978-0-12-407724-9.00006-9

69. Tang J, Chang Y, Aggarwal C, Liu H. A survey of signed network mining in social media. ACM Comput Surveys (CSUR). (2016) 49:1–37. doi: 10.1145/2956185

70. Griffiths MD. En “komponenter” modell for avhengighet innenfor en biopsykososial ramme. J Subst-bruk. (2005) 10:191–7.

71. Lin CY, Broström A, Griffiths MD, Pakpour AH. Investigating mediated effects of fear of COVID-19 and COVID-19 misunderstanding in the association between problematic social media use, psychological distress, and insomnia. Internet Intervent. (2020) 21:100345. doi: 10.1016/j.invent.2020.100345

72. Monacis L, De Palo V, Griffiths MD, Sinatra M. Social networking addiction, attachment style, and validation of the Italian version of the Bergen Social Media Addiction Scale. J Behav Addict. (2017) 6:178–86. doi: 10.1556/2006.6.2017.023

73. Gupta K. What is social media? How is it affecting adolescent's mental health? Eur Biomedical J. (2015) 410:51–65. doi: 10.1000/182

74. Zhao Z, Zhu H, Xue Z, Liu Z, Tian J, Chua MCH, et al. An image-text consistency driven multimodal sentiment analysis approach for social media. Inf Process Manag. (2019) 56:102097. doi: 10.1016/j.ipm.2019.102097

75. Small H. Co-citation in the scientific literature: a new measure of the relationship between two documents. J Am Soc Informat Sci. (1973) 24:265–9. doi: 10.1002/asi.4630240406

76. Su W, Han X, Yu H, Wu Y, Potenza MN. Do men become addicted to internet gaming and women to social media? A meta-analysis examining gender-related differences in specific internet addiction. Comput Human Behav. (2020) 113:106480. doi: 10.1016/j.chb.2020.106480

77. Albort-Morant G, Henseler J, Leal-Millán A, Cepeda-Carrión G. Mapping the field: a bibliometric analysis of green innovation. Sustainability . (2017) 9:1011. doi: 10.3390/su9061011

78. Wilson K, Fornasier S, White KM. Psychological predictors of young adults' use of social networking sites. Cyberpsychol Behav Social Netw . (2010) 13:173–7. doi: 10.1089/cyber.2009.0094

79. Barbera LD, Paglia LF, Valsavoia R. Social network and addiction. Stud Health Technol Inform. (2009) 144:33–6. doi: 10.3390/ijerph14030311

80. Hussain Z, Griffiths MD. Problematic social networking site use and comorbid psychiatric disorders: a systematic review of recent large-scale studies. Front Psychiatr . (2018) 5:686. doi: 10.3389/fpsyt.2018.00686

81. American Academy of Pediatric Dentistry. Guideline on Psychological Therapy . Chicago: American Academy of Pediatric (2008). Available online at: http://www.aapd.org/media/Policies_Guidelines/PsychologicalTherapy.pdf (accessed July 18, 2022).

82. Statista. Number of Smartphone Subscriptions Worldwide from 2016 to 2026. (2022). Available online at: https://www.statista.com/statistics/330695/number-of-smartphone-users-worldwide/ (accessed July 10, 2022).

83. Statista.com. Share of Global Mobile Website Traffic 2015–2021. (2022). Available online at: https://www.statista.com/statistics/277125/share-of-website-traffic-coming-from-mobile-devices/ (accessed July 18, 2022).

84. Statista.com. Global Social Media Traffic 2015–2021. (2022). Available online at: https://www.statista.com/statistics/277115/social-media-traffic/ (accessed July 18, 2022).

85. Statista.com. Asia Social Media Traffic 2015–2021. (2022). Available online at: https://www.statista.com/statistics/27751/asia-socia-media-traffic/ (accessed July 18, 2022).

86. Statista.com. Asia Website Traffic 2015-2021. (2022). Available online at: https://www.statista.com/statistics/277557/global-asia-website-traffic/ (accessed July 18, 2022).

87. de Valle MK, Gallego-García M, Williamson P, Wade TD. Social media, body image, and the question of causation: Meta-analyses of experimental and longitudinal evidence. Body Image. (2021) 39:276–92. doi: 10.1016/j.bodyim.2021.10.001

88. Koetsier M. Current issues of social media and crisis communication. Soc Media Crises Commun. (2022) 20–32.

89. Paschke K, Austermann MI, Thomasius R. ICD-11-based assessment of social media use disorder in adolescents: development and validation of the social media use disorder scale for adolescents. Front Psychiatr. (2021) 12:661483. doi: 10.3389/fpsyt.2021.661483

90. Horwitz J. The Facebook Whistleblower, Frances Haugen, Says She Wants to Fix the Company, Not Harm It . New York, NY: The Wall Street Journal (2021). p. 3.

91. Raschke RL, Krishen AS, Kachroo P. Understanding the components of information privacy threats for location-based services. J Informat Syst. (2014) 28:227–42. doi: 10.2308/isys-50696

Keywords: bibliometric analysis, social media, social media addiction, problematic social media use, research trends

Citation: Pellegrino A, Stasi A and Bhatiasevi V (2022) Research trends in social media addiction and problematic social media use: A bibliometric analysis. Front. Psychiatry 13:1017506. doi: 10.3389/fpsyt.2022.1017506

Received: 12 August 2022; Accepted: 24 October 2022; Published: 10 November 2022.

Reviewed by:

Copyright © 2022 Pellegrino, Stasi and Bhatiasevi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Alfonso Pellegrino, alfonso.pellegrino@sasin.edu ; Veera Bhatiasevi, veera.bhatiasevi@mahidol.ac.th

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Official websites use .gov

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

Secure .gov websites use HTTPS

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

Frequent Social Media Use and Experiences with Bullying Victimization, Persistent Feelings of Sadness or Hopelessness, and Suicide Risk Among High School Students — Youth Risk Behavior Survey, United States, 2023

Supplements / October 10, 2024 / 73(4);23–30

Please note:  This report has been corrected.

Emily Young, MSEd, MPH 1 ; Jessica L. McCain, PhD 2 ; Melissa C. Mercado, PhD 2 ; Michael F. Ballesteros, PhD 3 ; Shamia Moore, MPH 3 ; Laima Licitis, MPH 1 ,4 ; Joi Stinson, MPH 1 ,4 ; Sherry Everett Jones, PhD 1 ; Natalie J. Wilkins, PhD 1 ( View author affiliations )

Introduction

Limitations, future directions, acknowledgments.

• Article PDF
• Full Issue PDF

Social media has become a pervasive presence in everyday life, including among youths. In 2023, for the first time, CDC’s nationally representative Youth Risk Behavior Survey included an item assessing U.S. high school students’ frequency of social media use. Data from this survey were used to estimate the prevalence of frequent social media use (i.e., used social media at least several times a day) among high school students and associations between frequent social media use and experiences with bullying victimization, persistent feelings of sadness or hopelessness, and suicide risk. All prevalence estimates and measures of association used Taylor series linearization. Prevalence ratios were calculated using logistic regression with predicted marginals. Overall, 77.0% of students reported frequent social media use, with observed differences by sex, sexual identity, and racial and ethnic identity. Frequent social media use was associated with a higher prevalence of bullying victimization at school and electronically, persistent feelings of sadness or hopelessness, and some suicide risk among students (considering attempting suicide and having made a suicide plan), both overall and in stratified models. This analysis characterizes the potential harms of frequent social media use for adolescent health among a nationally representative sample of U.S. high school students. Findings might support multisectoral efforts to create safer digital environments for youths, including decision-making about social media policies, practices, and protections.

Social media, defined as “Internet-based channels that allow users to opportunistically interact and selectively self-present, either in real-time or asynchronously, with both broad and narrow audiences who derive value from user-generated content and the perception of interaction with others , ” has become a pervasive presence in everyday life, including among youths ( 1 ). Recent data indicate that approximately 95% of high school–aged youths use a social media platform, with approximately one fifth reporting “almost constant” social media use ( 2 ). Associations between frequent social media use and poor mental health outcomes among adolescents, including depression ( 3 ) and suicide risk ( 4 ), are being increasingly documented. Social media use might also increase risk for electronic victimization and perpetration ( 5 ), which can be antecedents of poor mental health. Evidence suggests that certain youth populations might be more vulnerable than others to potential harms of social media use, such as female and lesbian, gay, bisexual, transgender, and queer or questioning adolescents, who are more likely to experience electronic victimization than male or heterosexual peers ( 5 – 7 ). However, youths might also benefit from social support and connection found online ( 4 , 8 ). Understanding potential risks and benefits of social media use is critical for preparing youths to safely engage in an increasingly digitalized world.

This report uses 2023 Youth Risk Behavior Survey (YRBS) data to build on extant literature by examining associations between frequent social media use and U.S. high school students’ experiences of bullying victimization, persistent feelings of sadness or hopelessness, and suicide risk. Understanding such patterns and relations might guide public health practitioners’ efforts to prevent violence and injury and promote mental health, in line with Healthy People 2030 objectives ( https://health.gov/healthypeople ). Findings from this report might also support multilevel decision-making about social media use and cross-sectoral initiatives (e.g., education, technology, and policy) to create safer digital environments for youths.

Data Source

This report includes data from the 2023 YRBS (N = 20,103), a cross-sectional, school-based survey conducted biennially since 1991. Each survey year, CDC collects data from a nationally representative sample of public and private school students in grades 9–12 in the 50 U.S. states and the District of Columbia. Additional information about YRBS sampling, data collection, response rates, and processing is available in the overview report of this supplement ( 9 ). The prevalence estimates for frequent social media use for the study population overall and stratified by sex, race and ethnicity, grade, and sexual identity are available at https://nccd.cdc.gov/youthonline/App/Default.aspx . The full YRBS questionnaire, data sets, and documentation are available at https://www.cdc.gov/yrbs/index.html . Institutional reviews boards at CDC and ICF, the survey contractor, approved the protocol for YRBS. Data collection was conducted consistent with applicable Federal law and CDC policy.*

The primary exposure, frequency of social media use, was derived from the question, “How often do you use social media?” On the basis of response patterns, responses were dichotomized to reflect whether students used social media at least several times a day (frequent social media use [yes or no]) ( Table 1 ). Six health behaviors or experiences were measured and dichotomized: bullying victimization (bullied at school or electronically bullied; past 12 months [yes or no]), mental health (persistent feelings of sadness or hopelessness; past 12 months [yes or no]), and suicide risk (seriously considered attempting suicide, made a suicide plan, or attempted suicide; past 12 months [yes or no]) ( Table 2 ). The 2023 YRBS questionnaire defined bullying as “when one or more students tease, threaten, spread rumors about, hit, shove, or hurt another student over and over again. It is not bullying when two students of about the same strength or power argue or fight or tease each other in a friendly way.”

Demographic variables included sex (female or male), race and ethnicity, age group (≤14, 15, 16, 17, or ≥18 years), and sexual identity (heterosexual [straight], lesbian or gay, bisexual, questioning [I am not sure about my sexual identity/questioning], or described identity in some other way [I describe my identity some other way]). In the 2023 YRBS, sexual identity and gender identity were measured separately; only sexual identity is included in this analysis. Race and ethnicity were coded as American Indian or Alaska Native (AI/AN), Asian, Black or African American (Black), Native Hawaiian or other Pacific Islander (NH/OPI), White, Hispanic or Latino (Hispanic), or multiracial (selected more than one racial category). (Persons of Hispanic or Latino origin might be of any race but are categorized as Hispanic; all racial groups are non-Hispanic).

Descriptive analyses examined point prevalence estimates and corresponding 95% CIs for frequent social media use in the overall sample and by demographic characteristics. Chi-square tests and pairwise t -tests were used to compare demographic group differences. Associations between frequent social media use and health behaviors and experiences (bullying victimization, persistent feelings of sadness or hopelessness, and suicide risk) were assessed in overall and separate logistic regression models stratified by sex or sexual identity, which generated prevalence ratios (PRs) and adjusted PRs (aPRs) for each health behavior and experience. All models were adjusted for demographic variables of race and ethnicity, age, sex, and sexual identity. If a model was stratified by a demographic characteristic, then the model was not adjusted for this characteristic. All prevalence estimates and measures of association used Taylor series linearization. Prevalence ratios were calculated using logistic regression with predicted marginals. Estimates were considered statistically significant if the aPR 95% CIs did not include 1.0 or p value was <0.05. All analyses were conducted in SAS-callable SUDAAN (version 11.0.3; RTI International) using sample weights to account for complex survey design and nonresponse.

Overall, 77.0% of U.S. high school students reported using social media at least several times a day (i.e., frequent social media use) ( Table 3 ). Frequent social media use was more prevalent among female students compared with male students (81.8% versus 72.9%). Heterosexual students reported higher prevalence of frequent social media use than lesbian or gay students (79.2% versus 67.7%). Lesbian or gay students also reported lower prevalence of frequent social media use than students who identified as bisexual (82.2%), questioning (82.6%), or described their sexual identity in some other way (78.8%). AI/AN students had lower prevalence of frequent social media use (53.0%) than Asian, Black, White, Hispanic, or multiracial students.

Students who reported frequent social media use were more likely to be bullied at school and electronically bullied compared with less frequent social media users ( Table 4 ). Frequent social media users also were more likely to report persistent feelings of sadness or hopelessness. Frequent social media use was associated with having seriously considered attempting suicide and having made a suicide plan.

In sex-stratified analysis, female students who reported frequent social media use were more likely to experience bullying victimization at school and electronically compared with less frequent female social media users ( Table 5 ). Female students who reported frequent social media use were also more likely to report persistent feelings of sadness or hopelessness and having seriously considered attempting suicide. Among male students, frequent social media users were more likely to experience bullying victimization electronically. Male students who frequently used social media also were more likely to report persistent feelings of sadness or hopelessness and having seriously considered attempting suicide.

In sexual identity–stratified analyses, students who identified as lesbian or gay, bisexual, questioning, or described their identity in some other way (LGBQ+) and who reported frequent social media use were more likely to experience bullying victimization electronically and persistent feelings of sadness or hopelessness than less frequent LGBQ+ social media users ( Table 6 ). Among heterosexual students, both unadjusted and adjusted analyses found that those who were frequent social media users were more likely than less frequent social media users to experience all observed health behaviors and experiences except for attempted suicide.

This report provides the first national prevalence estimate of social media use from a representative sample of U.S. high school students. Findings suggest that most high school students use social media, and that a substantial majority (77.0%) use social media frequently (i.e., at least several times a day) (Table 1). Frequent social media use was largely consistent across demographic characteristics, highlighting the widespread presence of social media during adolescence. Therefore, it remains critical to strengthen collective understanding of potential risks and benefits of social media use for adolescent health and development, and in turn, understand how to create safe digital environments and help youths develop and maintain healthy digital practices that minimize harm ( 1 ).

Certain differences in students’ social media use by sex, racial and ethnic identity, and sexual identity were observed. In alignment with previous literature, female students reported higher prevalence of frequent social media use than male students ( 6 ). AI/AN students reported less frequent social media use compared with those of other racial and ethnic identities, which might reflect differences in broadband Internet access between rural and tribal communities and other communities in the United States ( 10 ). Lesbian and gay students reported less frequent social media use compared with peers of other sexual identities. This finding contrasts with certain previous literature indicating that lesbian, gay, and bisexual youths might spend more time engaging with identity-affirming communities online, often through social media ( 8 ). Further research is needed to understand nuances of social media use among youths and the impact of social media on health and well-being for different youth populations.

Consistent with previous research, frequent social media users were more likely to experience bullying victimization ( 5 ). Previous research has demonstrated evidence of overlap between in-person and electronic bullying contexts, with perpetrators of in-person bullying more likely to perpetrate electronic bullying, and victims of in-person bullying more likely to experience electronic bullying victimization and engage in bullying perpetration ( 11 ). Such interplay between in-person and electronic bullying environments might explain the finding of higher prevalence of bullying at school among frequent versus less frequent social media users. However, additional research is needed to better understand this phenomenon and the compounding impact of bullying victimization across multiple contexts on adolescents’ short- and long-term thriving ( 11 ).

Associations between frequent social media use and bullying victimization differed by sex and sexual identity. Female students who reported frequent social media use were more susceptible to bullying victimization compared with less frequent female social media users. This might reflect the types of victimization (e.g., relational and psychological) commonly experienced by adolescent girls ( 12 ), which are suited to digital environments that reduce barriers to conflict (e.g., anonymity and proximity). Among LGBQ+ students, frequent social media users were more likely to experience electronic bullying victimization than less frequent social media users yet demonstrated no significant differences in bullying victimization at school. In contrast, heterosexual students who used social media frequently were more likely to experience both types of bullying victimization compared with heterosexual students who used social media less often. One possible explanation is that LGBQ+ students who use social media frequently have greater exposure to online discrimination or stigma-based bullying victimization beyond school networks ( 7 , 8 ). Therefore, frequent and less frequent social media users could share similar experiences of bullying in at-school networks but different experiences electronically. Further research is needed to understand variations in at-school and electronic networks for youths of different identities and how overlap between at-school and electronic networks might influence bullying victimization.

In alignment with existing research, findings in this report support associations between adolescent social media use and mental health; specifically, frequent social media users were more likely to report persistent feelings of sadness or hopelessness ( 3 ). Adjusted stratified analyses demonstrated consistent associations across groups, conveying a shared risk for poor mental health among students who are frequent social media users. However, literature also suggests that certain groups are more vulnerable to the potential negative mental health impacts of social media than others (e.g., adolescent girls) ( 6 ). In this study, approximately half of female students and one third of LGBQ+ students who frequently used social media reported persistent feelings of sadness or hopelessness, respectively. Findings warrant more rigorous analyses inclusive of multiple mental health indicators to better understand differential impact of frequent social media use by sex, sexual identity, and other key demographic characteristics.

Overall, frequent social media users were more likely to report having seriously considered attempting suicide and having made a suicide plan. No significant differences in reports of attempted suicide by frequency of social media use were observed, perhaps because of the rarity of this behavior in the sample. These findings mirror broader inconsistencies in the literature ( 4 , 13 ). Certain researchers posit that the relation between social media use and suicide risk is more complex and indirect than a dose-response phenomenon ( 4 , 13 ). For example, differences in how adolescents are exposed to suicide-related content have been demonstrated to influence suicide risk. More interactive and proximate exposures via online discussion forums or suicide clusters might increase risk compared with passive media consumption ( 4 , 14 ). In addition, analyses did not describe indirect pathways (e.g., through online victimization or reduced sleep quality) through which frequent social media use might influence mental health and suicide risk, or protective factors (e.g., connectedness to others) that might buffer the negative impacts of frequent social media use on mental health and suicide risk ( 4 ). Because of persistent concerns about the impact of social media on youth mental health ( 1 ), additional research is needed to better understand how such pathways might moderate the relation between frequent social media use and suicide risk.

In stratified analyses, associations between frequent social media use and suicide risk diminished, except for heterosexual students. This group might be a factor in the small, significant association between social media use and making a suicide plan observed in the overall sample. Findings suggest that heterosexual students might be more vulnerable to negative impacts of social media on suicide risk. This is surprising because of high prevalence of suicide risk among LGBQ+ students in the sample, but also suggests that social media might not be the most influential factor of suicide risk for LGBQ+ students. Emerging literature has found that social media can be protective for youths who identify as LGBTQ+ by connecting them with affirming communities, support networks, and resources online ( 8 ) and might even reduce suicide risk for certain youths ( 4 ). More research is needed to understand potential protective effects of positive connections made through safe and supportive social media environments and their associations with bullying victimization, suicide risk, and mental health.

General limitations of the YRBS are available in the overview report of this supplement ( 9 ). Findings in this report are subject to at least six additional limitations. First, YRBS data are cross-sectional; causality and directionality of associations between frequent social media use and health behaviors and experiences cannot be established. Second, YRBS examples of social media were not exhaustive; students might engage in other online platforms that were not considered in responses to the social media item. Third, differences between social media nonusers and infrequent users might be masked. Responses to the social media item were dichotomized to ensure sufficient statistical power, and respondents who selected “I do not use social media” were grouped with less frequent social media users (Table 1). Fourth, to maintain consistency in recall period across health behaviors and experiences, analyses only included one mental health indicator; students reporting on other indicators of poor mental health might have been missed. Fifth, sexual identities were dichotomized into two broad categories in stratified analysis because of sample size limitations. Because of significantly lower prevalence of frequent social media use among lesbian and gay students, combining them with students of other sexual identities might have hidden possible stronger effects or differences for other identities. Finally, with the availability of social media, bullying victimization at school can occur in person or electronically; similarly, electronic bullying can happen at school or elsewhere. Therefore, the two bullying victimization measures (i.e., at school and electronically) might not be mutually exclusive because these two pathways of bullying might overlap.

Findings from this study highlight key areas for future research and practice regarding youth social media use and related health behaviors and experiences. This study identified important differences in frequent social media use and its impact on bullying victimization, persistent feelings of sadness and hopelessness, and suicide risk by sex and sexual identity; however, consensus is lacking about how best to measure social media use ( 3 , 4 ). Future research that identifies how different social media measures (e.g., frequency of use, passive versus active use, and addiction to use) might differentially describe social media and related health outcomes is important to further understanding of potential risks and benefits of youth social media use. In addition, these findings warrant additional exploration of the differential association of social media use with bullying, mental health, and suicide risk by racial and ethnic identity of youths along with more detailed analyses of differences by sexual identity and gender identity. Investigating such associations among frequent social media users might increase understanding about which students are more vulnerable to the negative impacts of frequent social media use. Future research exploring the pathways through which social media use might lead to poor mental health and suicide risk, including through cyberbullying and victimization, also is needed.

Improved understanding of youths’ social media use and related health outcomes can strengthen cross-sectoral endeavors to create safer digital environments, such as consumer safety policies, media literacy education and standards, and platform-based protections for youths online ( 1 ). This understanding might also help empower youths and families to make informed decisions about social media use and online behaviors that reduce risk for negative health outcomes, including bullying victimization, poor mental health, and suicide ( 1 ). School-based interventions that address bullying and suicide prevention have been proven to be effective ( 15 , 16 ). Strengthening youths’ health-enhancing skills, creating protective environments, and promoting connections to positive adults and peers through programs such as What Works in Schools ( https://www.cdc.gov/healthyyouth/whatworks/index.htm ) can help reduce risk for multiple forms of violence and suicide ( 17 ). CDC’s Community Violence Prevention Resource for Action ( https://www.cdc.gov/violence-prevention/media/pdf/resources-for-action/CV-Prevention-Resource-for-Action_508.pdf ) and Suicide Prevention Resource for Action ( https://www.cdc.gov/suicide/resources/prevention.html ) contain strategies based on the best available evidence to reduce community violence, including youth violence and bullying, and suicide. StopBullying.gov ( https://www.stopbullying.gov/prevention/how-to-prevent-bullying ) provides steps that schools, youths, and their families can take to prevent bullying, including setting clear behavioral expectations and promoting empathy, self-awareness, and self-regulation skills. The U.S. Surgeon General’s Advisory on Social Media and Youth Mental Health ( https://www.hhs.gov/surgeongeneral/priorities/youth-mental-health/social-media/index.html#action ) and American Academy of Pediatrics’ Center of Excellence on Social Media and Youth Mental Health ( https://www.aap.org/en/patient-care/media-and-children/center-of-excellence-on-social-media-and-youth-mental-health ) provide recommendations on ways youths and families can reduce risk for harm from social media use (e.g., developing family media plans to promote healthy social media use). More research is needed to rigorously test and evaluate interventions that incorporate evidence-based prevention strategies among youths who use social media, particularly those at increased risk for harms associated with frequent social media use.

Overall, approximately three fourths of U.S. high school students reported using social media at least several times a day. Frequent social media use among students was associated with higher prevalence of bullying victimization at school and electronically, persistent feelings of sadness and hopelessness, having seriously considered attempting suicide, and having made a suicide plan. Associations between frequent social media use and these health behaviors and experiences differed by sex and sexual identity. Although additional research is needed to understand precisely how social media use differentially affects adolescent risk for bullying victimization, poor mental health, and suicide, existing evidence-based prevention strategies can be used by families, schools, and communities to promote adolescent mental health and prevent injury and violence.

David Chyen, William A. Harris, Connie Lim, Cecily K. Mbaka, Zachary Myles, Lindsay Trujillo.

Corresponding author: Emily Young, Division of Adolescent and School Health, National Center for Chronic Disease Prevention and Health Promotion, CDC. Telephone: 404-718-3672; Email: [email protected] .

1 Division of Adolescent and School Health, National Center for Chronic Disease Prevention and Health Promotion, CDC, Atlanta, Georgia; 2 Division of Violence Prevention, National Center for Injury Prevention and Control, CDC, Atlanta, Georgia; 3 Division of Injury Prevention, National Center for Injury Prevention and Control, CDC, Atlanta, Georgia; 4 Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee

Conflicts of Interest

All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. No potential conflicts of interest were disclosed.

* 45 C.F.R. part 46.114; 21 C.F.R. part 56.114.

• Office of the Surgeon General. Social media and youth mental health: the U.S. Surgeon General’s advisory. Washington DC: US Department of Health and Human Services, Office of the Surgeon General; 2023. https://www.hhs.gov/sites/default/files/sg-youth-mental-health-social-media-advisory.pdf
• Anderson M, Faverio M, Gottfried J. Teens, social media and technology 2023. Washington, DC: Pew Research Center; 2023. https://www.pewresearch.org/internet/2023/12/11/teens-social-media-and-technology-2023
• Keles B, McCrae N, Grealish A. A systematic review: the influence of social media on depression, anxiety and psychological distress in adolescents. Int J Adolesc Youth 2020;25:79–93. https://doi.org/10.1080/02673843.2019.1590851
• Macrynikola N, Auad E, Menjivar J, Miranda R. Does social media use confer suicide risk? A systematic review of the evidence. Comput Hum Behav Rep 2021;3:100094. https://doi.org/10.1016/j.chbr.2021.100094
• Craig W, Boniel-Nissim M, King N, et al. Social media use and cyber-bullying: a cross-national analysis of young people in 42 countries. J Adolesc Health 2020;66(6S):S100–8. https://doi.org/10.1016/j.jadohealth.2020.03.006 PMID:32446603
• Twenge JM, Haidt J, Lozano J, Cummins KM. Specification curve analysis shows that social media use is linked to poor mental health, especially among girls. Acta Psychol (Amst) 2022;224:103512. https://doi.org/10.1016/j.actpsy.2022.103512 PMID:35101738
• Abreu RL, Kenny MC. Cyberbullying and LGBTQ youth: a systematic literature review and recommendations for prevention and intervention. J Child Adolesc Trauma 2017;11:81–97. https://doi.org/10.1007/s40653-017-0175-7 PMID:32318140
• Berger MN, Taba M, Marino JL, Lim MSC, Skinner SR. Social media use and health and well-being of lesbian, gay, bisexual, transgender, and queer youth: systematic review. J Med Internet Res 2022;24:e38449. https://doi.org/10.2196/38449 PMID:36129741
• Brener ND, Mpofu JJ, Krause KH, et al. Overview and methods for the Youth Risk Behavior Surveillance System—United States, 2023. In: Youth Risk Behavior Surveillance—United States, 2023. MMWR Suppl 2024;73(No. Suppl-4):1–12.
• Early J, Hernandez A. Digital disenfranchisement and COVID-19: broadband Internet access as a social determinant of health. Health Promot Pract 2021;22:605–10. https://doi.org/10.1177/15248399211014490 PMID:33955266
• Lazuras L, Barkoukis V, Tsorbatzoudis T. Face-to-face bullying and cyberbullying in adolescents: trans-contextual effects and role overlap. Technol Soc 2017;48:97–104. https://doi.org/10.1016/j.techsoc.2016.12.001
• National Academies of Sciences, Engineering, and Medicine. Preventing bullying through science, policy, and practice. Washington DC: National Academies Press; 2016.
• Nesi J, Burke TA, Bettis AH, et al. Social media use and self-injurious thoughts and behaviors: a systematic review and meta-analysis. Clin Psychol Rev 2021;87:102038. https://doi.org/10.1016/j.cpr.2021.102038 PMID:34034038
• Swedo EA, Beauregard JL, de Fijter S, et al. Associations between social media and suicidal behaviors during a youth suicide cluster in Ohio. J Adolesc Health 2021;68:308–16. https://doi.org/10.1016/j.jadohealth.2020.05.049 PMID:32646827
• Community Preventive Services Task Force. Violence prevention: school-based anti-bullying interventions. Washington, DC: US Department of Health and Human Services, Community Preventive Services Task Force; 2022. https://www.thecommunityguide.org/findings/violence-prevention-school-based-anti-bullying-interventions.html
• Community Preventive Services Task Force. Mental health: multi-tiered trauma-informed school programs to improve mental health among youth. Washington, DC: US Department of Health and Human Services, Community Preventive Services Task Force; 2023. https://www.thecommunityguide.org/findings/mental-health-multi-tiered-trauma-informed-school-programs-improve-mental-health-among-youth.html
• Wilkins NJ, Rasberry C, Liddon N, et al. Addressing HIV/sexually transmitted diseases and pregnancy prevention through schools: an approach for strengthening education, health services, and school environments that promote adolescent sexual health and well-being. J Adolesc Health 2022;70:540–9. https://doi.org/10.1016/j.jadohealth.2021.05.017 PMID:35305791

* N = 20,103 respondents.

* The 2023 National Youth Risk Behavior Survey questionnaire describes social media “such as Instagram, TikTok, Snapchat, and Twitter.”

* N = 20,103 respondents. The total number of students answering each question varied. Data might be missing because 1) the question did not appear in that student’s questionnaire, 2) the student did not answer the question, or 3) the response was set to missing because of an out-of-range response or logical inconsistency. Percentages in each category are calculated on the known data. A total of 15,203 students responded to the social media item. † Unweighted. § Weighted. ¶ Chi-square tests were applied to examine the bivariate relations between demographic characteristics and frequency of social media use. Statistical significance is defined as p<0.05 for the chi-square test. ** Female students significantly differed from male students for prevalence of using of social media at least several times a day based on t -test with Taylor series linearization (p<0.05). †† Persons of Hispanic or Latino origin might be of any race but are categorized as Hispanic; all racial groups are non-Hispanic. §§ American Indian or Alaska Native students significantly differed from Asian, Black or African American, White, Hispanic or Latino, and multiracial students for prevalence of using social media at least several times a day based on t -test with Taylor series linearization (p<0.05). ¶¶ Heterosexual (straight) students significantly differed from lesbian or gay students for prevalence of using social media at least several times a day based on t -test with Taylor series linearization (p<0.05). *** Lesbian or gay students significantly differed from bisexual and questioning students and students who described identity in some other way for prevalence of using social media at least several times a day based on t -test with Taylor series linearization (p<0.05).

Abbreviations : aPR = adjusted prevalence ratio; PR = prevalence ratio. * N = 20,103 respondents. The total number of students answering each question varied. Data might be missing because 1) the question did not appear in that student’s questionnaire, 2) the student did not answer the question, or 3) the response was set to missing because of an out-of-range response or logical inconsistency. Percentages in each category are calculated on the known data. A total of 15,203 students responded to the social media item. † Logistic regression models estimated health behaviors and experiences between those who did and did not use social media at least several times a day. § Adjusted for age, race and ethnicity, sex, and sexual identity estimated health behaviors and experiences behaviors between those who did and did not use social media at least several times a day. ¶ Estimates were considered statistically significant if the 95% CIs did not include 1.0. Certain statistically significant aPRs have 95% CIs that include 1.0 because of rounding.

Abbreviations : PR = prevalence ratio; aPR = adjusted prevalence ratio. * N = 20,103 respondents. The total number of students answering each question varied. Data might be missing because 1) the question did not appear in that student’s questionnaire, 2) the student did not answer the question, or 3) the response was set to missing because of an out-of-range response or logical inconsistency. Percentages in each category are calculated on the known data. A total of 15,203 students responded to the social media question. † Logistic regression models estimated health behaviors and experiences between those who did and did not use social media at least several times a day, among female students. § Adjusted for age, race and ethnicity, and sexual identity estimated health behaviors and experiences between those who did and did not use social media at least several times a day, among female students. ¶ Logistic regression models estimated health behaviors and experiences between those who did and did not use social media at least several times a day, among male students. ** Adjusted for age, race and ethnicity, and sexual identity estimated health behaviors and experiences between those who did and did not use social media at least several times a day, among male students. †† Estimates were considered statistically significant if the 95% CIs did not include 1.0. Certain statistically significant aPRs have 95% CIs that include 1.0 because of rounding.

Abbreviations : aPR = adjusted prevalence ratio LGBQ+ = lesbian or gay, bisexual, questioning, or described identity in some other way; PR = prevalence ratio. * N = 20,103 respondents. The total number of students answering each question varied. Data might be missing because 1) the question did not appear in that student’s questionnaire, 2) the student did not answer the question, or 3) the response was set to missing because of an out-of-range response or logical inconsistency. Percentages in each category are calculated on the known data. A total of 15,203 students responded to the social media question. † Logistic regression models estimated health behaviors and experiences between those who did and did not use social media at least several times a day, among LGBQ+ students. § Adjusted for age, race and ethnicity, and sex estimated health behaviors and experiences between those who did and did not use social media at least several times a day, among LGBQ+ students. ¶ Logistic models estimated health behaviors and experiences between those who did and did not use social media at least several times a day, among heterosexual students. ** Adjusted for age, race and ethnicity, and sex estimated health behaviors and experiences between those who did and did not use social media at least several times a day, among heterosexual students. †† Estimates were considered statistically significant if the 95% CIs did not include 1.0. Certain statistically significant aPRs have 95% CIs that include 1.0 because of rounding.

Suggested citation for this article: Young E, McCain JL, Mercado MC, et al. Frequent Social Media Use and Experiences with Bullying Victimization, Persistent Feelings of Sadness or Hopelessness, and Suicide Risk Among High School Students — Youth Risk Behavior Survey, United States, 2023. MMWR Suppl 2024;73(Suppl-4):23–30. DOI: http://dx.doi.org/10.15585/mmwr.su7304a3 .

MMWR and Morbidity and Mortality Weekly Report are service marks of the U.S. Department of Health and Human Services. Use of trade names and commercial sources is for identification only and does not imply endorsement by the U.S. Department of Health and Human Services. References to non-CDC sites on the Internet are provided as a service to MMWR readers and do not constitute or imply endorsement of these organizations or their programs by CDC or the U.S. Department of Health and Human Services. CDC is not responsible for the content of pages found at these sites. URL addresses listed in MMWR were current as of the date of publication.

All HTML versions of MMWR articles are generated from final proofs through an automated process. This conversion might result in character translation or format errors in the HTML version. Users are referred to the electronic PDF version ( https://www.cdc.gov/mmwr ) and/or the original MMWR paper copy for printable versions of official text, figures, and tables.

An official website of the United States government

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

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

• Publications
• Account settings
• Advanced Search
• Journal List

Social media use in healthcare: A systematic review of effects on patients and on their relationship with healthcare professionals

Edin smailhodzic, wyanda hooijsma, albert boonstra, david j langley.

• Author information
• Article notes
• Copyright and License information

Corresponding author.

Received 2015 Nov 17; Accepted 2016 Aug 18; Collection date 2016.

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated.

Since the emergence of social media in 2004, a growing percentage of patients use this technology for health related reasons. To reflect on the alleged beneficial and potentially harmful effects of social media use by patients, the aim of this paper is to provide an overview of the extant literature on the effects of social media use for health related reasons on patients and their relationship with healthcare professionals.

We conducted a systematic literature review on empirical research regarding the effects of social media use by patients for health related reasons. The papers we included met the following selection criteria: (1) published in a peer-reviewed journal, (2) written in English, (3) full text available to the researcher, (4) contain primary empirical data, (5) the users of social media are patients, (6) the effects of patients using social media are clearly stated, (7) satisfy established quality criteria.

Initially, a total of 1,743 articles were identified from which 22 were included in the study. From these articles six categories of patients’ use of social media were identified, namely: emotional, information, esteem, network support, social comparison and emotional expression. The types of use were found to lead to seven identified types of effects on patients, namely improved self-management and control, enhanced psychological well-being, and enhanced subjective well-being, diminished subjective well-being, addiction to social media, loss of privacy, and being targeted for promotion. Social media use by patients was found to affect the healthcare professional and patient relationship, by leading to more equal communication between the patient and healthcare professional, increased switching of doctors, harmonious relationships, and suboptimal interaction between the patient and healthcare professional.

Conclusions

Our review provides insights into the emerging utilization of social media in healthcare. In particular, it identifies types of use by patients as well as the effects of such use, which may differ between patients and doctors. Accordingly, our results framework and propositions can serve to guide future research, and they also have practical implications for healthcare providers and policy makers.

Electronic supplementary material

The online version of this article (doi:10.1186/s12913-016-1691-0) contains supplementary material, which is available to authorized users.

Keywords: Social media, Health, Patients, Healthcare professionals

Previous studies on social media use in healthcare identified different effects of social media use by patients for health related reasons within the healthcare system. Social media can serve as an aid to patients. For example, it fosters their autonomy by complementing the information provided by healthcare professionals [ 1 ] and by providing psychosocial support [ 2 ]. Social media use by patients can also be an aid to healthcare professionals by providing a tool to strengthen the organization’s market position [ 3 , 4 ] and stimulating conversation for brand building and improved service delivery [ 4 , 5 ]. In fact, social media may have effects on both patients, and on the wider healthcare system [ 6 ]. In particular, it allows patients to receive support [ 1 ], and to complement offline information [ 2 ], which may lead to enhancing the empowerment of patients [ 6 ]. However, social media use by patients does not only provide beneficial effects. It may also constitute a challenge within the healthcare system to both patients and healthcare professionals. Since everybody with access to social media can post “advice” on how to deal with a certain health condition, it is important to create reliable online communication channels to prevent health problems being exacerbated [ 7 ]. For example, one misguided idea on Twitter urged Nigerians to drink excessive amounts of salt water to combat Ebola. However, this may have led to two deaths and more than 12 admissions to hospital [ 7 ]. Thus, many healthcare professionals fear that social media use by patients for health related purposes often spreads misinformation among patients [ 1 ].

Use of social media by patients for health related reasons provides different effects, which can result in both benefits and challenges. It is important to identify these effects of social media for the healthcare system, as “a growing percentage of patients use social media for health-related reasons, so health professionals will have to reflect on the alleged beneficial effects and the potential harmful effects of social media use by patients in healthcare” [ 8 ]. Hence, the review of these effects will contribute to a better understanding of potential benefits and challenges for both patients and healthcare professionals, but also other healthcare actors such as policy makers.

Therefore, this paper provides a systematic literature review of empirical studies on the effects of social media use by patients for health related reasons on patients and on their relationships with healthcare professionals. To our knowledge no other systematic research on this topic has been performed to date. Such review also provides the opportunity to extract general findings from the studies. Subsequently, healthcare professionals can learn from these findings about the effects of social media use by patients and share this knowledge with other patients and use it to their own advantage. We aim to answer the following question:

According to recent empirical research, what are the effects of social media use by patients for health related reasons on patients and on their relationships with healthcare professionals?

To answer this question, the paper will address the following: (1) the types of social media use by patients (2) the identified effects of social media use by patient on patients (3) the identified effects on the relationship between patients and their healthcare professionals and (4) the relationship between the effects on patients and healthcare professionals. By addressing the issue (4), we attempt to bring together our findings from the issues (2) and (3) and explore linking mechanisms between the effects patients experience and their subsequent link to the effects they experience in relationship with the healthcare professionals.

Study aim and terminology

The aim of this paper is to gain insights in the benefits and challenges of the effects of social media use by patients within the healthcare system and especially the effects on patients and on their relationships with healthcare professionals. The effects we focus on in this paper can be both causal and reciprocal, but always start with the use of social media by patients.

Despite the popularity of social media, there is a confusion about what is exactly meant by the term social media. Therefore, in this paper we use the definition provided in the highly cited paper by Kaplan and Haenlein [ 9 ]. They describe social media as “a group of Internet-based applications that build on the ideological and technological foundations of Web 2.0, and that allow the creation and exchange of User Generated Content”. The internet-based applications refer to the different categories of social media, which are blogs, content communities, social networking sites, collaborative projects, virtual game worlds and virtual social worlds. These types of social media are accessible to users to utilize for, among other things, health related reasons.

The term “users of social media in healthcare” in this paper refer to the patients and their family members. Patients are treated as any person who self-proclaims to be suffering from a certain condition, whether officially diagnosed by a healthcare professional or not. We define healthcare professionals as those who study, advise on or provide preventive, curative, rehabilitative and promotional health services based on an extensive body of theoretical and factual knowledge in diagnosis and treatment of conditions and other health problems [ 10 ].

In order to provide an overview of the different effects of social media use by patients for health related reasons on patients and on their relationships with healthcare professionals, we conducted systematic literature review.

To identify the articles, we employed a search strategy consisting of three terms as follows

a) “social media” or blog* or “content communit*” or “social networking site*” or “online social network*” or “virtual world*” or “online communit*” or “online forum*” or Facebook or Twitter or Wikipedia or IMVU or “second life” or YouTube b) “Patient*” and c) “health* provider*” or “health* professional*” or “physician*” or “doctor*” or “hospital*”. The full search string is also included in the Appendix A (see Additional file 1 ). Additionally, as suggested by the referees of this paper, we also used the term “client*” instead of “patient*”, together with the other two original categories of terms.

To perform this literature review, we followed the guidelines on conducting a systematic literature review as prescribed by the Preferred Reporting Items for Systematic Literature Reviews and Meta-Analyses (PRISMA) [ 11 ].

To conduct the search, we chose relevant databases of Web of Science and EBSCOhost COMPLETE. By focusing on EBSCOhostCOMPLETE, we made sure that the healthcare databases are included such as “PsycINFO”, “CINAHL” and “MEDLINE”. We also included the databases such “Business source premier” to include findings with a business perspective. Search options were slightly different for each database. For EBSCO the irrelevant databases were excluded first and no specific search field was selected for one of the three terms. The list of databases is presented in the Appendix B (See Additional file 2 ). Additionally, the option to search only in scholarly (peer reviewed) journals was used and the publication dates were selected to be after 2004. In the year 2004 the term Web 2.0 was used for the first time, which marks the start of the social media era [ 9 ]. On the other hand, we selected topic for all three terms in the Web of Science, which included the titles, abstracts, author keywords, and keywords plus fields of the articles.

Selection criteria

For an article to be included in the study it had to meet several selection criteria as follows: (1) published in a peer-reviewed journal, (2) written in English, (3) full text available to the researcher, (4) contain primary empirical data, (5) the users of social media are patients, (6) the effects of patients using social media are clearly stated, (7) satisfy established quality criteria. The articles were assessed on their quality by using the standard quality assessment criteria as identified by [ 12 ].

Prior to final screening and selection of the papers, first and second author agreed to independently read 100 abstracts and select the articles that would be included in the study based on the selection criteria. Afterwards, the selected articles by the two authors were compared and there was complete concurrence on the category “yes, this one will be included”. For some of the articles that were marked as “maybe”, first and second author had a brief discussion to reach a consensus. This helped to reach higher reliability for the inclusion of the articles. Further in the process, the second author consulted the first author whenever there was a doubt whether to include or exclude the article. In addition, regular meetings with the third author also contributed to the overall process of the selection.

Data analysis

The resulting papers were characterized by the research aim and the type of research, which is reflected in the Table  1 . The papers were further categorized according to the focus of the research question and data. Each paper’s empirical findings were categorized by looking at data and making first notes inductively. Following this, we looked at our notes on topics that emerged from analysed articles and compared them to earlier literature. In this way, concepts from prior literature helped us to make the sense of data from different articles and categorize them. A good example for that is the concept of social support, which we used to classify types of use. After analysing the articles in this way, we formulated propositions in the discussion section.

Overview of included studies in the literature review

Search results

The searches were carried out in the period ending on March 17th, 2015. The application of the search strategy to the two search engines resulted initially in a total of 1,743 articles. Within the 1,743 articles many duplicates were found as well within the search engines as between the search engines. By removing duplicates the first found article was kept. In this way, we identified and removed 468 duplicates leaving us with 1,275 articles.

The remaining 1,275 articles were screened on title and abstract with regards to the selection criteria. Whenever we had doubts if an article is relevant or when title and abstract were not clear, we inspected the paper in more details by accessing full article. An article was removed when, for example, it became clear that the user of social media was not a patient but another user, like the hospital, a regular “healthy” person or healthcare professional. Additionally, several articles referred to internet use by patients for health related reasons and their effects, but did not specify the effects of social media. Therefore, such articles were removed. Moreover, articles that were written in a language other than English as well as articles that did not comprise primary data or did not elaborate on an effect of patients using social media. This left us with 22 articles that met our criteria. In addition, as a result of the referees’ suggestion to include term “client”, we identified one additional article, making the entire list of 23 articles for the quality assessment.

Quality of the articles was assessed by using the Standard Quality Assessment Criteria for Evaluating Primary Research Papers by [ 12 ] as presented in the Appendix C (See Additional file 3 ). This assessment tool distinguishes between qualitative and quantitative research and provides different quality assessment criteria for each type of research. The criteria are rated on their presence in the respective article and are either completely addressed in the article (resulting in 2 points), partly addressed (resulting in 1 point), or not addressed (resulting in 0 points). In case an article scored below the threshold of a 50 % score of the total amount of points possible, the article is assumed to be of low quality and removed from this paper. This cut-off point for inclusion is relatively liberal according to the authors of the assessment tool [ 12 ]. One article had a quality score below the 50 % cut-point and was excluded, which left us with the total of 22 articles for analysis.

The article selection process is shown in Fig.  1 .

Flowchart of study selection process

Overview of the articles

The Table  1 provides an overview of 22 articles included in the study. All studies except for three were published in or after 2010. Moreover, 19 articles were published in journals that are related to the medical field, whereas only three articles are published in journal that do not have a specific connection to medicine: Journal of Sociology, New Review of Hypermedia & Multimedia, and Information Research. Only two out of the 22 articles use a theory or a model to build their research on, namely the concept of masculinity [ 13 ] and the actant model [ 14 ]. The group of articles consists of nine quantitative, seven qualitative and six mixed methods studies.

The analysis of articles with regard to the type of social media and conditions is presented in the Appendix D (See Additional file 4 ), which shows that the 12 articles studied online support communities and most focused on chronic conditions. Other types of social media platforms and conditions were spread among the remaining articles.

Analysis of results

This section presents findings from 22 articles we included in our study. First of all, an overview of the extracted findings is presented regarding the types of social media use by patients. Following this, we present the effects of social media use on patients. Subsequently, an overview of the extracted findings regarding effects of social media use by patients on the relationship between patients and healthcare professionals are presented, discussed, and categorized.

Types of social media use by patients for health related reasons

Our analysis starts with the type of use and motivation for their use of social media. When analysing all articles it becomes clear that patients do not use social media to circumvent healthcare professionals, but rather use it as a complement to healthcare professional services to fulfil the patients’ needs that cannot be met by the healthcare professional. The relationship between patients and healthcare professionals is viewed by the patients as a more clinical one, where healthcare professionals provide expert knowledge about the condition and recommend treatment based on their medical knowledge, but not on their first-hand experience [ 15 ].

Additionally, doctors often have difficulty expressing empathy and that they filter information for the patient, where the patient would rather be informed about all options. Patients also believe that doctors might not be aware of the latest breakthroughs [ 15 ]. Moreover, one of the the main reasons for patients to join online health communities is their dissatisfaction with their healthcare professional’s inability to meet the patients’ emotional and informational needs [ 1 ]. Another reason for patients to use social media was to bridge the gap between traditional health information about their condition and everyday life [ 16 ]. In particular, Facebook is seen as an important addition to traditional in-office counselling in improving patient knowledge [ 17 ].

Therefore, the types of social media use by patients as identified in this paper refer to the way in which patients use social media intended to meet an unfulfilled need. These are identified in the articles are categorized as shown in Table  2 and explained below. Categories represent social support, consisting of emotional, esteem, informational, and network support [ 18 ], and other types of use, which are emotional expression and social comparison.

Types of use of social media by patients for health related purposes by article

Social support

The most common type of social media use by patients for health related reasons that we found is social support. Social support is defined as “the process of interaction in relationships which is intended to improve coping, esteem, belonging, and competence through actual or perceived exchanges of psychosocial resources” [ 19 ]. Social support is represented through five different categories and four of these categories were found to be common types of social media use by patients for health related purposes [ 18 ]. These four types, namely emotional support, esteem support, information support, and network support are explained below.

Emotional support . Emotional support is defined as “communication that meets an individual’s emotional or affective needs” [ 20 ]. It refers to support gained through expressions of care and concern, which serve to improve an individual’s mood. Emotional support helps patients to meet their emotional or affective needs. The use of social media by patients for emotional support was identified in 13 articles. Examples of emotional support are “sharing of emotional difficulties” [ 21 ], “encountering support that feels like a warm blanket wrapped around you” [ 22 ], and “share emotions with other people who are coping with similar problems” [ 23 ].

Esteem support . Esteem support refers to “communication that bolsters an individual’s self-esteem or beliefs in their ability to handle a problem or perform a needed task” [ 20 ]. The aim of this type of support is to encourage individuals to take the actions needed to successfully live with their condition. The use of social media by patients for esteem support was identified in seven articles. Examples of esteem support include “getting support from other patient’s encouragement” [ 24 ], “share experiences about a new treatment to find encouragement before starting it” [ 25 ], and “rituals of confirming each other’s endeavours to follow health instructions” [ 14 ].

Information support . Information support is “communication that provides useful or needed information” [ 20 ]. In particular, newly diagnosed patients are in a need for a lot of information about their condition and treatment options, which can be provided by patients who have already dealt with the condition for a longer period [ 20 ]. The use of social media by patients for information support was identified in all articles. Examples of information support are “receiving advice about treatments” [ 26 ], “help fellow sufferers by sharing experiences and relevant information about the disease” [ 24 ], and “ask questions about the condition” [ 25 ].

Network support . Network support is defined as “communication that affirms an individual’s belonging to a network or reminds him/her of support available from the network” [ 20 ]. Hence, network support is support that reminds people that no matter what situation they are facing, they are not alone. The use of social media by patients for network support was identified in 13 articles. Examples of network support include “meeting other patients who had gone through similar experiences” [ 27 ], “a means to connect with others in similar situations” [ 15 ], and “fostering relationships based on shared attributes” [ 28 ].

Other types of use

In addition to the social support, we also identified two other types of use, which could not be directly placed under one of the subcategories of social support. These are emotional expression and social comparison.

Emotional expression . Emotional expression refers to the unique opportunity provided by social media for patients (and other users) to express their emotions freely without having to be concerned about the immediate feelings or reactions of those who stand close to them. As noted in one of the articles, “online communities provide the potential to allow patients to open up and reduce the inhibitions felt in sharing experiences in face to face situations”, e.g. hurting other people’s feelings [ 13 ]. Therefore, patients can use social media as a place to express their emotions freely, like, releasing negative emotions [ 24 ]. In contrast to emotional support, which is defined as patients interacting in and receiving communication to meet their affective needs, emotional expression refers to patients expressing their emotions regardless of whether someone will respond. The use of social media by patients for emotional expression was identified in 8 articles. Examples include “a place to vent about the illness” [ 25 ] and “an outlet for expressing your emotions freely” [ 15 ].

Social comparison . Patients use social media to compare themselves with other patients to see how “bad” their condition is or to find out how the treatments work. This social comparison can seem to overlap with social support, for instance, when patients compare themselves to peers to recognize that they are not the only person in this situation (network support) or when patients compare themselves to peers to find out how other people suffer from or cope with the condition (esteem support, emotional support, or information support). However, social comparison was categorized separately as within the articles the authors presented it as a different type of use without specifying the details. The use of social media by patients for social comparison was identified in four articles. Examples include “upward social comparison” [ 25 ] and “comparison with other members [ 23 ].

Effects of the different types of social media use by patients on patients

In this section the effects of the use of social media by patients for health related reasons are analysed and presented. The most common effect of patients using social media for health related reasons is patient empowerment, which is represented through three categories: enhanced subjective well-being, enhanced psychological well-being, and improved self-management and control. We also identified four other types of effects, which are less common in our literature review. These are: diminished subjective well-being, loss of privacy, addiction to social media, and being targeted for promotion. Identified categories are presented in Table  3 and explained below.

Effects of social media use by patients for health related reasons by article

Patient empowerment

In current literature, the concept of empowerment is defined as “an individual trait, characterized by an emphasis on increased individual control over the aspects of one’s life” [ 29 ]. We argue that the patient empowerment refers to “the discovery and development of one’s inherent capacity to be responsible for one’s own life. Hence, patients are empowered when they are in possession of the knowledge, skills, and self-awareness necessary to identify and attain their own goals” [ 14 ]. Information support, esteem support, and emotional support were significant predictors of a patient’s sense of empowerment [ 30 ]. Informational support was the strongest predictor of increased sense of empowerment followed by esteem support and emotional support. The three subcategories of empowerment, namely enhanced subjective well-being, enhanced psychological well-being, and improved self-management and control, are discussed below.

Enhanced subjective well-being . Subjective well-being refers to “what people think and how they feel about their lives in positive ways” [ 31 ]. In this paper, enhanced subjective well-being mainly refers to the pleasant emotions patients experience due to their social media use for health related reasons. “People experience enhanced subjective well-being when they feel many pleasant and few unpleasant emotions” [ 31 ]. Consequently, enhanced subjective well-being refers to an increase in the experience of pleasant emotions, which in turn heightens people’s feeling of empowerment. The effect enhanced subjective well-being was identified in 12 articles. Examples from the articles concerning enhanced subjective well-being are “increased optimism” [ 22 ], “increased acceptance of the illness” [ 23 ], “decrease anxiety” [ 26 ] and “increased sense of normalcy” [ 27 ].

Enhanced psychological well-being . Psychological well-being is defined in the literature as “focusing on eudemonic well-being, which is the fulfilment of human potential and a meaningful life” [ 32 ]. One of the components affecting psychological well-being is the experience of positive relations with others. It is argued that a central component of mental health is to be in warm, trusting, interpersonal relations [ 33 ]. Moreover, “self-actualizers are described as having strong feelings of empathy and affection for all human beings and as being capable of greater love, deeper friendship, and more complete identification with others” [ 33 ]. Therefore, enhanced psychological well-being refers to an increase in the patient’s experience of positive relations with others through the use social media. The effect enhanced psychological well-being was identified in 14 articles. Examples from the articles include “feeling of being connected to other people” [ 34 ], “increased social network online as well as offline” [ 27 ], and “promotion of deep relationships” [ 15 ].

Improved self-management and control . Improved self-management and sense of control refers to the improvement in the capability of patients to better handle their condition. As patients feel better informed, their ability to make decisions on their own improves, which fosters self-management and perceived control over the condition. Ability to deal with the day-to-day life with the condition also increases, for example due to learning about coping strategies, which also fosters improved self-management and perceived control. The effect of improved self-management and sense of control was identified in 14 articles. Examples from the articles include “increase patient’s self-management” [ 34 ], “improvement in the ability to manage the disease” [ 16 ], and “fostering insight and universality” [ 26 ].

Other types of effects

In addition to the patient empowerment, several other types of effects of social media use by patients on patients were identified. These are diminished subjective well-being, loss of privacy, being targeted for promotion, and addiction to social media.

Diminished subjective well-being . Diminished subjective well-being is opposite of enhanced subjective well-being and indicates an increase in the experience of negative emotions due to the use of social media, such as an increase in feelings of worry and anxiety. It was identified in six articles. Diminished subjective well-being was the most common found effect of patients using social media for health related reasons. Examples include “demoralization” [ 25 ], “hurt feelings due to negative feedback” [ 16 ], and “increased feelings of anxiety” [ 35 ].

Loss of privacy . Loss of privacy was mentioned in only one article [ 16 ]. It refers to the finding that the patients lose their privacy when they post personal videos on YouTube.

Being targeted for promotion . Being targeted for promotion was also mentioned in only one article by [ 16 ]. It refers to the finding that patients who post videos on YouTube can be targets product promotions.

Addiction to social media . Addiction was an effect identified in one article by [ 35 ]. It refers to the finding that sometimes patients experience their social media use for health related reasons to be addictive. As such, it often took the time that they usually spent doing other tasks.

Effects of social media use by patients on the relationship between patients and healthcare professionals

The use of social media by patients for health related reasons does not only affect the patients themselves or other patients, but also the relationship between patients and healthcare professionals. In total, nine articles discussed the effects of social media use by patients on the relationship between patients and healthcare professionals, although six out of these nine articles only touch very briefly upon this subject. The effects of social media use by patients for health related reasons on the relationship between patients and healthcare professionals that have been extracted from the articles are presented in Table  4 and discussed below.

Effects of social media use by patients on the healthcare professional – patient relationship

The findings presented in Table  4 are divided into categories representing the effects on the relationship between patients and healthcare professionals. These categories are more equal communication between the patient and healthcare professional, increased switching of doctors, harmonious relationships, and suboptimal interaction between the patient and healthcare professional. The categories are discussed below.

More equal communication between the patient and healthcare professional

Social media use by patients for health related reasons can lead to more equal communication between the patient and healthcare professional. This effect refers to patients feeling more confident in their relationship with the healthcare professional. In total, five articles referred to this effect. With the information from the social media platforms, patients can increase their knowledge about treatment options. Consequently, they are better able to communicate with the healthcare professional as they can better understand their condition [ 36 ]. Hence, patients may feel more confident in their relationship with their physician [ 22 , 23 ]. Patients feel that they are better prepared for consultations as they are more informed about their condition and know better what questions to ask [ 23 ]. Social support received through the use of social media eventually increases the likeliness to form an intention to actively communicate with the doctor during a medical consultation [ 30 ]. Moreover, the use of social media provides the opportunity to learn and increase health communication, which may lead to an increase in the patients’ willingness to seek medical attention [ 37 ]. Hence, these findings suggest that the use of social media for health related purposes can increase a patient’s confidence and active communication in their relationship with healthcare.

Increased switching of doctors

Social media use by patients for health related reasons can lead to shorter relationships between healthcare professionals and patients. Patients may change doctor due to online discussions about physicians or due to negative reactions from doctors about the patients’ treatments supervised by their regular physicians. Two articles found that patients changed physician because of those patients’ use of social media. For example, negative reactions from physicians to the mentions of social media use by patients made the patients to look for second opinion and even change their doctor [ 1 ]. On the other hand, some patients changed their doctor as a result of online discussion with other patients [ 36 ].

Harmonious relationships

Harmonious relationships between healthcare professionals and patients can be established as social media provide a place for patients to release negative emotions. However, the effect of harmonious relationships also comprises the fact that social media might empower individuals to follow doctor’s recommendations, which reduces discussions during clinical interaction. The effect of harmonious relationships was identified in two articles. Social media provide a place for patients to express their emotions and maintain harmony in the relationship between healthcare professional and patient in offline consultations, which focuses on non-emotional aspects of the disease [ 24 ]. On the other hand, social media were empowering individual users to comply with doctors’ recommendations as a group, which affects the healthcare professional patient relationship by potentially reducing discussions during clinical interactions as patients stick to the recommended treatment [ 14 ]. However, it can also be viewed as a missed opportunity, as patients do not empower each other to find alternative treatments [ 14 ].

Suboptimal interaction between the patient and healthcare professional

As patients use social media for health related reasons, this can affect the patient and healthcare professional relationship by leading to suboptimal interaction between the patient and healthcare professional. When patients bring social media content to the consultation, this can lead to increased processes of sorting information, transforming the potential risk to the healthcare professional, and challenging the healthcare professional’s expertise [ 13 ]. Additionally, if the healthcare professional reacts negatively to what patient learned from social media, this might decrease the patient’s subjective well-being [ 1 ]. The effect of suboptimal interaction between the patient and healthcare professional was identified in two articles. Discussion of the information from social media during the consultation was experienced as a threat by the physician [ 13 ]. Furthermore, healthcare professionals reacted negatively to online health community content raised during clinical interactions, which made patients feel disempowered, but it did not change their online behaviour [ 1 ].

Relationship between effects on patients and effects on the patient healthcare professional relationship

In the section about the effect of “more equal communication between the patient and healthcare professional”, we already mentioned that increased communication during a consultation on behalf of the patient can be caused by patient empowerment. Patient empowerment refers to “the inherent capacity to be responsible for one’s own life” [ 14 ]. In regards to the relationship between patients and healthcare professionals, the patients took more responsibility for their own condition. Five articles find that the patient empowerment indeed affects the patients’ confidence, ability and willingness to actively participate in clinical interactions. Patients increased their sense of empowerment through their intention to actively communicate with the doctor [ 30 ]. Additionally, the patient empowerment was associated with an increased confidence in dealing with the physician [ 23 ]. Moreover, the convenience of social media use by patients is that it reduces the information gap between healthcare professionals and patients and patients have a better understanding of the healthcare professional during consultations [ 37 ]. Social media can empower patients by giving them access to information and opportunities for discussions, which increases the patient’s involvement in clinical interactions [ 15 ]. Finally, the patient empowerment increases the ability of patients to communicate with the healthcare professionals [ 22 ]. Hence, we argue that the patient empowerment contributes to more equal communication between the patient and the healthcare professional.

This review provides an insight into the current body of knowledge on the effects of social media use by patients for health related reasons and the effects on patients and on their relationship with healthcare professionals. All of the studies were published in the past 10 years, with only three articles published before 2010. This can be explained by a recent increase in the use of social media by patients for health related reasons.

We categorized articles into different types of use and effects. We identified that the most common type of use was social support, namely emotional support, esteem support, information support, and network support. The types of social media use were most often found to affect patients by empowering them through enhanced subjective well-being, enhanced psychological well-being, and improved self-management and control. However, the types of social media use by patients were also found to affect patients through addiction to social media, diminished subjective well-being, being targeted for promotion, and loss of privacy. Moreover, the identified types of social media use by patients for health related reasons was also found to affect the relationship between patients and healthcare professionals as it can result in more equal communication between the patient and healthcare professional, shorter relationships, harmonious relationships, and suboptimal interaction between the patient and healthcare professional. Based on these findings, we made three propositions.

Relationship between use and effect: Network support and enhanced psychological well-being

When patients are diagnosed with a certain condition that nobody in their close (offline) network has experienced before, patients can feel very lonely [ 27 ]. As a diabetic patient states “I literally felt like the only diabetic on the planet” [ 16 ]. However, social media provide an opportunity to easily connect with others and reduce this feeling of loneliness. Consequently, patients using social media for network support enhanced their psychological well-being. For example, social media provide means to connect with others in similar situations and this can break a patient’s loneliness [ 15 ]. This is in line with earlier studies that have shown how the existence of network support contributes to a better well-being of the patients [ 41 , 42 ]. Interestingly, [ 41 ] suggest that the network support may not only benefit the patients themselves, but also their families who care for them. Yet, the relationship between the network support and psychological well-being may depend on the level of self-esteem. For example, college students with low self-esteem profited more from online social networking sites for bridging social capital and starting relationships than college students with high self-esteem [ 43 ]. In line with that, social networking sites provides the unique opportunity for patients to be able to talk about the sensitive aspects of the condition, as online communities provide the potential to reduce inhibitions felt in sharing experiences face to face [ 13 ]. Such an inhibition could reflect low self-esteem in terms of a reluctance to talk about the condition in face to face conversations.

Proposition 1: Social media use by patients for network support leads to enhanced psychological well-being. This effect is stronger for people with low self-esteem than for the people with high self-esteem.

Relationship between content and effect: Reading other people’s stories, improved self-management and control and enhanced subjective well-being

Not all patients that make use of social media use it actively. Sometimes patients only use social media to read about other people’s stories, without actively contributing themselves. These people are called lurkers. The lurking behaviour may be related to the level of privacy concerns and computer anxiety [ 44 ]. In particular, anxiety leads to increase in lurking. Two articles in our sample were focused on the effects of patients using social media merely by reading other people’s stories. From the two articles, it becomes clear that the effects experienced by reading other people’s stories are being better informed [ 22 , 26 ]. Additionally, by reading other people’s stories anxiety was found to significantly decrease [ 26 ]. Consequently, these findings suggest that reading other people’s stories on social media can lead to enhanced subjective well-being and improved self-management and control. However, [ 22 ] and [ 26 ] do not elaborate on the content of the stories read. Contrasting findings were found in other articles regarding how content affects the effects of reading other people’s stories. For example, cancer patients who read other people’s stories enhanced their subjective well-being [ 24 ]. Reading about success stories was found to enhance confidence to fight the condition, whereas reading about bad experiences prepared the patient mentally for difficult times ahead. On the other hand, the patients suffering from an inflammatory bowel disease who read other people’s stories about a bad experience suffered from diminished subjective well-being [ 25 ]. This is in line with earlier findings showing that the lack of sharing and feedback on this sharing may threaten the need for belonging [ 45 ]. Finally, patients suffering from infertility experienced diminished subjective well-being as the result of reading other people’s stories [ 35 ]. Reading stories about successful pregnancies led to increased feelings of jealousy, pain and a sense of alienation, whereas reading about bad experiences led to increased feelings of worry, anxiety and decreased optimism. Thus, this may lead to diminished subjective well-being. On the other hand, one study in our sample shows that this actually may enhance subjective-well-being [ 24 ]. In particular, this paper focused on blogs whereas other studies focused on online support groups [ 24 ]. Among other uses, blogs can be used as personals diaries to express thoughts, feelings, and stories [ 9 ]. Level of distress actually decreases when people blog about their emotional difficulties [ 46 ].

Proposition 2: Reading other people’s stories about a negative experience leads to diminished subjective well-being. This effect is weaker for patients who blog about their experiences than for those who do not.

Relationship between patients and healthcare professionals: shift in power balance and increased quality of decision making

The effects of social media use by patients for health related reasons show that social media use by patients can lead to patient empowerment. Patient empowerment is an established concept in the medical research and has been promoted to foster patient autonomy [ 47 ]. As a result of the patient empowerment, patients may increasingly interact with their healthcare professional and get more involved in the decision making process [ 15 ]. In this case, social media can be seen as a “new” technology adopted by patients, which may shift the power balance between the healthcare professional and the patient. The use of new technologies in healthcare has been suggested as a way to empower end-consumers by enabling speed and convenience in accessing health related information [ 48 ]. In this line, the patients are able to actively participate in the interactions with healthcare professionals. On the other hand, the healthcare professionals may experience a decrease in power in the decision making process. According to the political variant of the interaction theory [ 49 ], “a product of the interaction of system features with the intra-organizational distribution of power, defined either objectively, in terms of horizontal or vertical power dimensions, or subjectively, in terms of symbolism can be resistance to the system”. Hence, redistribution of power between patients and healthcare professionals may cause the resistance from healthcare professionals. Yet, the role of health professionals has to change because embracing patient empowerment in healthcare means making a change, which sometimes seem difficult due to traditional approach, which is embedded in their current training [ 50 ].

However, increased patient involvement in the clinical interaction could potentially increase the risk placed on the healthcare professionals [ 13 ]. Healthcare professional may not be in complete control of the information used during decision making as the patient also has a voice, but the healthcare professional bears full responsibility for the decision taken. When patients bring in the information from social media to the consultation, this could lead to unnecessary processes of sorting relevant information from irrelevant information and can be experienced as challenging the healthcare professional’s expertise [ 1 , 13 ]. Hence, based on these findings it is possible for healthcare professionals to resist this shift in the balance of power. However, increased equalization of the healthcare professional and patient communication can be a positive and desired effect. In particular, healthcare professionals may become more patient-centred, thus complementing the patient empowerment [ 51 ]. As a consequence of patient empowerment, we propose that the quality of clinical decision making may be enhanced.

According to the concept of bounded rationality [ 52 ], not all information can be gained on all available treatment options by healthcare professionals, as the human mind has a limited capacity to process the available information and often time is limited as well. Hence, healthcare professionals are unable to know all the information regarding treatment options and the newest developments, which affects their decision making. Thus, patients can extend this information base of the healthcare professional by specializing themselves in their own condition. This could provide an opportunity to increase the quality of the treatment decisions.

Proposition 3: As a result of patient empowerment due to patients using social media for health related reasons, the power balance between healthcare professionals and patients becomes more equalized, leading to increased quality of clinical decisions making.

Notwithstanding the interesting results described above, this research has some limitations which, along with the three propositions, suggest opportunities for further research. It is possible that we missed some articles that could have used different terminology. Consequently, the results of this paper might not be generalizable for all social media platforms. For practical reasons, we excluded non-English papers. Finally, a limitation of every literature review is that the authors of the included articles will have had different objectives and used different methods and means of interpretation in reaching their conclusions. In this paper, we highlighted the most important findings on our topic of study and we categorized the key effects of social media use on patients and on their relationships with healthcare professionals.

The use of social media by patients for health related reasons is growing. This systematic literature review reflects on beneficial and potentially harmful effects of social media use by patients for health related. The findings show that patients use social media mainly for social support, which is represented through information support, emotional support, esteem support, and network support. Other identified types of social media use by patients have found to be emotional expression and social comparison. These types of social media use by patients were found to most commonly lead to patient empowerment. Other effects of social media use by patients we identified were diminished subjective well-being, addiction to social media, being targeted for promotion, and loss of privacy. The types of social media use by patients were also found to affect the healthcare professional and patient relationship by stimulating more equal communication between the patient and healthcare professional, shorter relationships, harmonious relationships, suboptimal interaction between the patient and healthcare professional. Whereas some of the articles discussed the effects of patients’ use of social media on relationship between patients and healthcare professionals briefly, we encourage future research to tackle this issue. We developed three propositions, which may also stimulate further research in this respect.

Acknowledgments

We thank Eveline Hage for providing insightful feedback in the course of manuscript preparation.

We have not received any funding for conducting this study.

Availability of data and materials

Materials and data used in this literature review may be obtained from the first author.

Authors’ contributions

ES was responsible for the research design, significantly contributed to the selection and analysis of included papers and reworked an earlier draft of the manuscript. WH contributed with the paper selection and analysis and wrote a preliminary draft of the manuscript. AB made significant contributions to the framework for analysis, interpretation of selected papers and writing the manuscript. DJL made significant contribution to interpretation of the studies and participated in writing the final version of the manuscript. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Additional files.

Appendix A-Search string. (DOCX 14 kb)

Appendix B-List of databases. (DOCX 14 kb)

Appendix C-Quality assessment [ 53 ]. (DOCX 21 kb)

Appendix D-Summary of articles per social media category. (DOCX 14 kb)

Contributor Information

Edin Smailhodzic, Email: [email protected].

Wyanda Hooijsma, Email: [email protected].

Albert Boonstra, Email: [email protected].

David J. Langley, Email: [email protected]

• 1. Rupert DJ, Moultrie RR, Read JG, Amoozegar JB, Bornkessel AS, Donoghue AC, Sullivan HW. Perceived healthcare provider reactions to patient and caregiver use of online health communities. Patient Educ Couns. 2014;96(3):320–326. doi: 10.1016/j.pec.2014.05.015. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 2. Ho YX, O’Connor BH, Mulvaney SA. Features of online health communities for adolescents with type 1 diabetes. West J Nurs Res. 2014;36(9):1183–1198. doi: 10.1177/0193945913520414. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 3. McCaughey D, Baumgardner C, Gaudes A, LaRochelle D, Jiaxin K, Wu KJ, Raichura T. Best practices in social media: Utilizing a value matrix to assess social media’s impact on health care. Soc Sci Comput Rev. 2014;32(5):575–589. doi: 10.1177/0894439314525332. [ DOI ] [ Google Scholar ]
• 4. Williams J. A new roadmap for healthcare business success. J Healthc Financ Manage Assoc. 2011;65(5):62–69. [ PubMed ] [ Google Scholar ]
• 5. Li C, Bernoff J. Groundswell: Winning in a world transformed by social technologies. Boston: Harvard Business School Press; 2008. [ Google Scholar ]
• 6. Hawn C. Take two aspirin and tweet me in the morning: How Twitter, Facebook, and other social media are reshaping health care. Health Aff. 2009;28(2):361–368. doi: 10.1377/hlthaff.28.2.361. [ DOI ] [ PubMed ] [ Google Scholar ]
• 7. Carter M. Medicine and the media: How Twitter may have helped Nigeria contain Ebola. Br Med J. 2014;349. [ DOI ] [ PubMed ]
• 8. Antheunis ML, Tates K, Nieboer TE. Patients’ and health professionals’ use of social media in health care: Motives, barriers and expectations. Patient Educ Couns. 2013;92(3):426–431. doi: 10.1016/j.pec.2013.06.020. [ DOI ] [ PubMed ] [ Google Scholar ]
• 9. Kaplan AM, Haenlein M. Users of the world, unite! The challenges and opportunities of Social Media. Bus Horiz. 2010;53:59–68. doi: 10.1016/j.bushor.2009.09.003. [ DOI ] [ Google Scholar ]
• 10. WHO . Classifying health workers: Mapping occupations to the international standard classification. 2010. [ Google Scholar ]
• 11. Moher D, Liberati A, Tetzlaff J, Altman DG. The PRISMA Group Preferred reporting items for systematic reviews and meta-analyses: The PRISMA Statement. Open Med. 2009;3(3):123–130. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 12. Kmet LM, Lee RC, Cook LS. Standard quality assessment criteria for evaluating primary research papers from a variety of fields. Alberta Herit Found Med Res. 2004;13:1–22. [ Google Scholar ]
• 13. Broom A. The eMale: Prostate cancer, masculinity, and online support as a challenge to medical expertise. J Sociol. 2005;41(1):87–104. doi: 10.1177/1440783305050965. [ DOI ] [ Google Scholar ]
• 14. Wentzer HS, Bygholm A. Narratives of empowerment and compliance: Studies of communication in online patient support groups. Int J Med Inform. 2013;82(12):386. doi: 10.1016/j.ijmedinf.2013.01.008. [ DOI ] [ PubMed ] [ Google Scholar ]
• 15. Colineau N, Paris C. Talking about your health to strangers: understanding the use of online social networks by patients. New Rev Hypermed Multimed. 2010;16(1):141–160. doi: 10.1080/13614568.2010.496131. [ DOI ] [ Google Scholar ]
• 16. Gómez-Zúñiga B, Fernandez-Luque L, Pousada M, Hernández-Encuentra E, Armayones M. ePatients on YouTube: Analysis of four experiences from the patients’ perspective. J Med Internet Res. 2012;14(2):26. doi: 10.2196/jmir.1968. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 17. Kofinas JD, Varrey A, Sapra KJ, Kanj RV, Chervenak FA, Asfaw T. Adjunctive social media for more effective contraceptive counseling. Obstet Gynecol. 2014;123(4):763–770. doi: 10.1097/AOG.0000000000000172. [ DOI ] [ PubMed ] [ Google Scholar ]
• 18. Schaefer C, Coyne JC, Lazarus RS. The health-related functions of social support. J Behav Med. 1981;4(4):381–406. doi: 10.1007/BF00846149. [ DOI ] [ PubMed ] [ Google Scholar ]
• 19. Cohen S, Underwood LG, Gottlieb BH. Social support measurement and intervention: a guide for health and social scientists. New York: Oxford; 2000. [ Google Scholar ]
• 20. Mattson M, Hall JG. Health as communication nexus: A service-learning approach. Dubuque: Kendall Hunt; 2011. [ Google Scholar ]
• 21. Menon IS, Sharma MK, Chandra PS, Thennarasu K. Social networking sites: An adjunctive treatment modality for psychological problems. Indian J Psychol Med. 2014;36(3):260–263. doi: 10.4103/0253-7176.135374. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 22. Van Uden-Kraan CF, Drossaert CHC, Taal E, Seydel ER, Van de Laar M. Self-reported differences in empowerment between lurkers and posters in online patient support groups. J Med Internet Res. 2008;10(2):18. doi: 10.2196/jmir.992. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 23. Bartlett YK, Coulson NS. An investigation into the empowerment effects of using online support groups and how this affects health professional/patient communication. Patient Educ Couns. 2011;83(1):113–119. doi: 10.1016/j.pec.2010.05.029. [ DOI ] [ PubMed ] [ Google Scholar ]
• 24. Chiu YC, Hsieh YL. Communication online with fellow cancer patients: Writing to be remembered, gain strength, and find survivors. J Health Psychol. 2013;18(12):1572–1581. doi: 10.1177/1359105312465915. [ DOI ] [ PubMed ] [ Google Scholar ]
• 25. Coulson NS. How do online patient support communities affect the experience of inflammatory bowel disease? An online survey. JRSM Short Reports. 2013;4:1–8. doi: 10.1177/2042533313478004. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 26. Setoyama Y, Yamazaki Y, Namayama K. Benefits of peer support in online Japanese breast cancer communities: Differences between lurkers and posters. J Med Internet Res. 2011;13(4):122. doi: 10.2196/jmir.1696. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 27. Bers MU, Beals LM, Chau C, Satoh K, Blume ED, DeMaso DR, Gonzalez-Heydrich J. Use of a virtual community as a psychosocial support system in pediatric transplantation. Pediatr Transplant. 2010;14(2):261–267. doi: 10.1111/j.1399-3046.2010.01271.x. [ DOI ] [ PubMed ] [ Google Scholar ]
• 28. Frost JH, Massagli MP. Social uses of personal health information within PatientsLikeMe, an online patient community: What can happen when patients have access to one another’s data. J Med Internet Res. 2008;10(3):15. doi: 10.2196/jmir.1053. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 29. Gutschoven K, Van den Bulck J. Towards the measurement of psychological health empowerment in the general public. 2006. [ Google Scholar ]
• 30. Oh HJ, Lee B. The effect of computer-mediated social support in online communities on patient empowerment and doctor–patient communication. Health Commun. 2012;27(1):30–41. doi: 10.1080/10410236.2011.567449. [ DOI ] [ PubMed ] [ Google Scholar ]
• 31. Sahoo FM, Mohapatra L. Psychological well-Being in professional groups. J Indian Acad Appl Psychol. 2009;35(2):211–217. [ Google Scholar ]
• 32. Chen FF, Jing Y, Hayes A, Lee JM. Two concepts or two approaches? A bifactor analysis of psychological and subjective well-being. J Happiness Stud. 2013;14(3):1033–1068. doi: 10.1007/s10902-012-9367-x. [ DOI ] [ Google Scholar ]
• 33. Ryff CD. Happiness is everything, or is it? Explorations on the meaning of psychological well-being. J Pers Soc Psychol. 1989;57(6):1069–1081. doi: 10.1037/0022-3514.57.6.1069. [ DOI ] [ Google Scholar ]
• 34. Bauer R, Bauer M, Spiessl H, Kagerbauer T. Cyber-support: An analysis of online self-help forums (online self-help forums in bipolar disorder) Nordic J Psychiatry. 2013;67(3):185–190. doi: 10.3109/08039488.2012.700734. [ DOI ] [ PubMed ] [ Google Scholar ]
• 35. Malik S, Coulson NS. ‘They all supported me but I felt like I suddenly didn’t belong anymore’: An exploration of perceived disadvantages to online support seeking. J Psychosom Obstet Gynecol. 2010;31(3):140–149. doi: 10.3109/0167482X.2010.504870. [ DOI ] [ PubMed ] [ Google Scholar ]
• 36. Wicks P, Massagli M, Frost J, Brownstein C, Okun S, Vaughan T, Bradley R, Heywood J. Sharing health data for better outcomes on PatientsLikeMe. J Med Internet Res. 2010;12(2):19. doi: 10.2196/jmir.1549. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 37. Lee YC, Wu WL. The effects of situated learning and health knowledge involvement on health communications. Reprod Health. 2014;11(1):220–233. doi: 10.1186/1742-4755-11-93. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 38. Kim S, Yoon JW. The use of an online forum for health information by married Korean women in the United States. Inf Res. 2012;17(2):514. [ Google Scholar ]
• 39. Pagoto S, Schneider KL, Evans M, Waring ME, Appelhans B, Busch AM, Whited MC, Thind H, Ziedonis M. Tweeting it off: Characteristics of adults who tweet about a weight loss attempt. J Am Med Infor Assoc. 2014;21(6):1032–1037. doi: 10.1136/amiajnl-2014-002652. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 40. Farber BA, Nitzburg GC. Young adult self-disclosures in psychotherapy and on Facebook. Couns Psychol Q. 2015;29(1):1–14. [ Google Scholar ]
• 41. Magliano L, Marasco C, Fiorillo A, Malangone C, Guarneri M, Maj M. The impact of professional and social network support on the burden of families of patients with schizophrenia in Italy. Acta Psychiatr Scand. 2002;106(4):291–298. doi: 10.1034/j.1600-0447.2002.02223.x. [ DOI ] [ PubMed ] [ Google Scholar ]
• 42. Blickem C, Kennedy A, Vassilev I, Morris R, Brooks H, Jariwala P, Blakeman T, Rogers A. “Linking people with long‐term health conditions to healthy community activities: development of Patient‐Led Assessment for Network Support (PLANS) Health Expect. 2013;16(3):48–59. doi: 10.1111/hex.12088. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 43. Steinfeld C, Ellison NB, Lampe C. Social capital, self-esteem, and use of online social network sites: A longitudinal analysis. J Appl Dev Psychol. 2008;29(6):434–445. doi: 10.1016/j.appdev.2008.07.002. [ DOI ] [ Google Scholar ]
• 44. Osatuyi B. Is lurking an anxiety-masking strategy on social media sites? The effects of lurking and computer anxiety on explaining information privacy concern on social media platforms. Comput Hum Behav. 2015;49:324–332. doi: 10.1016/j.chb.2015.02.062. [ DOI ] [ Google Scholar ]
• 45. Tobin SJ, Vanman EJ, Verreynne M, Saeri AK. Threats to belonging on Facebook: Lurking and ostracism. Soc Influ. 2015;10(1):31–42. doi: 10.1080/15534510.2014.893924. [ DOI ] [ Google Scholar ]
• 46. Boniel-Nissim M, Barak A. The therapeutic value of adolescents’ blogging about social–emotional difficulties. Psychol Serv. 2013;10(3):333–341. doi: 10.1037/a0026664. [ DOI ] [ PubMed ] [ Google Scholar ]
• 47. Feste C, Anderson RM. Empowerment: from philosophy to practice. Patient Educ Couns. 1995;26(1):139–44. doi: 10.1016/0738-3991(95)00730-N. [ DOI ] [ PubMed ] [ Google Scholar ]
• 48. Angaran DM. Telemedicine and telepharmacy: current status and future implications. Am J Health Syst Pharm. 1999;56(14):1405–26. doi: 10.1093/ajhp/56.14.1405. [ DOI ] [ PubMed ] [ Google Scholar ]
• 49. Markus ML. Power, politics, and MIS implementation. Commun ACM. 1983;26(6):430–444. doi: 10.1145/358141.358148. [ DOI ] [ Google Scholar ]
• 50. Anderson RM, Funnell MM. Patient empowerment: myths and misconceptions. Patient Educ Couns. 2010;79(3):277–82. doi: 10.1016/j.pec.2009.07.025. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 51. Holmström I, Röing M. The relation between patient-centeredness and patient empowerment: a discussion on concepts. Patient Educ Couns. 2010;79(2):167–72. doi: 10.1016/j.pec.2009.08.008. [ DOI ] [ PubMed ] [ Google Scholar ]
• 52. Simon HA. Decision and organization. Amsterdam: North-Holland Publishing; 1972. [ Google Scholar ]
• 53. Bletsos K, Alexias G, Tsekeris C. Towards a fourth cosmology of doctor-patient relationship: a reflection on the virtual patient community PatientsLikeMe. Open Access J Glob Sustain Infor Soc. 2013;11(1):136–144. [ Google Scholar ]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

• View on publisher site
• PDF (674.2 KB)
• Collections

Similar articles

Cited by other articles, links to ncbi databases.

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

Add to Collections