social media affects on mental health essay

Essay on Effects Of Social Media On Mental Health

Students are often asked to write an essay on Effects Of Social Media On Mental Health in their schools and colleges. And if you’re also looking for the same, we have created 100-word, 250-word, and 500-word essays on the topic.

Let’s take a look…

100 Words Essay on Effects Of Social Media On Mental Health

Introduction.

Social media is a tool that lets us communicate and share with people around the world. It’s very popular, especially with young people. But, it can also affect our mental health in different ways.

Positive Impact

Social media can have a good effect on our mental health. It allows us to connect with friends and family, even if they live far away. It can also help us find groups of people who share our interests. This can make us feel less alone and more understood.

Negative Impact

On the other hand, social media can also have a bad effect on our mental health. Seeing other people’s “perfect” lives can make us feel bad about our own. It can also lead to cyberbullying, which can hurt our self-esteem and cause stress.

Importance of Balance

Like most things, balance is key when using social media. Spending too much time online can make us feel isolated in real life. It’s important to take breaks and spend time doing other things we enjoy.

In conclusion, social media can have both positive and negative effects on our mental health. It’s important to use it wisely and remember that it’s okay to take a break if it’s making us feel bad.

250 Words Essay on Effects Of Social Media On Mental Health

Social media is a powerful tool that connects people from all corners of the world. It is a platform where we share our thoughts, ideas, and daily life activities. But, it also has a great impact on our mental health.

Impacts on Self-Esteem

One of the main effects of social media on mental health is on our self-esteem. When we see our friends having fun, achieving things, or looking happy, we often compare ourselves with them. This comparison can make us feel less confident and happy about our own lives.

Loneliness and Isolation

Another effect is the feeling of loneliness and isolation. Even though we are connected with many people online, we can still feel alone. This is because social media interactions are not the same as real-life interactions. This feeling can lead to sadness and depression.

Anxiety and Fear

Social media can also cause anxiety and fear. We often worry about what others will think of our posts. We fear negative comments and judgments. This constant worry can lead to stress and anxiety.

In conclusion, while social media has its benefits, it can also have negative effects on our mental health. It can affect our self-esteem, make us feel lonely, and cause anxiety. Therefore, it is important to use social media wisely and not let it control our lives. We need to remember that what we see on social media is not always the full picture of someone’s life.

500 Words Essay on Effects Of Social Media On Mental Health

Social media is a big part of our lives. We use it to chat with friends, share photos, and learn about the world. But, it can also affect our mental health in both good and bad ways. This essay will explore these effects.

Positive Effects

First, let’s talk about the good things. Social media can make us feel connected. It allows us to keep in touch with friends and family, even if they live far away. This can make us feel less lonely. It can also help us find people who share our interests. For example, if you love painting, you can join an art group online. This can boost your confidence and make you feel part of a community.

Negative Effects

Now, let’s look at the negative side. Spending too much time on social media can make us feel sad or anxious. This is because we often compare our lives to the perfect ones we see online. This can make us feel like we are not good enough.

Another problem is cyberbullying. Some people use social media to hurt others by sending mean messages or spreading rumors. This can lead to stress, anxiety, and even depression.

Impact on Sleep

Social media can also affect our sleep. Many of us check our phones before bed. The bright screen can make it harder for us to fall asleep. Lack of sleep can lead to mood swings, trouble focusing, and feeling tired all the time.

Ways to Stay Healthy

The good news is there are ways to use social media without hurting our mental health. One way is to limit our time online. We can set a timer or use an app that reminds us to take a break.

We should also remember that what we see online is not always real. Many people only share the best parts of their lives, not the hard parts.

If someone is being mean to us online, we should tell a trusted adult. We can also block or report the person.

In conclusion, social media has both good and bad effects on our mental health. It can make us feel connected, but it can also make us feel sad, anxious, and tired. To stay healthy, we need to use social media in a balanced way, remember that what we see online is not always real, and speak up if someone is being mean to us.

That’s it! I hope the essay helped you.

If you’re looking for more, here are essays on other interesting topics:

• Essay on Effects Of Social Media On Family Relationships
• Essay on Effects Of Social Media
• Essay on Effects Of Poverty On Health

Apart from these, you can look at all the essays by clicking here .

Happy studying!

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

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

Social Media and Mental Health Essay

The role of social media in people’s lives has increased exponentially over the past decade. The online personas that people create matter to them nearly just as much as their real-life image due to the constant communication and the opportunity to track down their responses to specific posts at any time. As a result, the impact of social media on the mental well-being of its users is worth considering. Sumner et al. point to the positive effects of social media, clarifying that the specified technological innovation can be used as the tool for improving mental health of its users. Namely, the research states that social media allows spreading useful and positive information about health-related issues much faster than traditional media. As a result, the opportunities for increasing the levels of public health and addressing some of the most common public health issues emerge.

The connection between the positivity of a message and its reception in social media is a crucial piece of information that needs to be incorporated into the current approach toward increasing the levels of public health, citizens’ health literacy, and the accessibility of health services. Namely, the conclusions that Sumner et al. make concerning the direct correlation between the positivity of a message and the likelihood of it being transmitted to a greater number of people should be used as the tool fro encouraging better health management: “Sheer volume of supportive content provided by produced by organizations or individuals may be less important than creating higher-quality messages” (p. 143). Thus, the conclusion that the authors provide should be used to enhance the efficacy and accessibility of the current health services.

One could argue that the general research outcomes should be seen as quite upsetting given the implications that they provide. Namely, the fact that the work of health professionals, who perform meticulous studies and arrange the data as carefully as possible to provide accurate and concise guidelines may be less important than an upbeat yet empty message is a rather sad idea. The specified conclusions may lead to a drop in the extent of health practitioners’ and nurse educators’ enthusiasm in providing the services of the highest quality.

However, the message that Sumner et al. convey could also be seen as an opportunity for enhancing health education and raising health literacy within the community by building a better rapport with its members. Namely, the data about the significance of the use of positivity in social networks as the tool for attracting the attention of patients and target audiences should be utilized to shape the current approach toward promoting health literacy. Specifically, healthcare practitioners and registered nurses, especially those that address the issues of patient education directly, need to create the strategy for the online conversation with patients through social media. The specified dialogue could be based on a combination of positive messages and clear visuals that inform patients about key issues in health management and provide them with an opportunity to improve their health literacy.

Additionally, the authors have provided an important tool for the development of a campaign aimed at public health management and improvement. Namely, based on the outcomes of the research carried out by Sumner et al. have informed the strategies for improving communication between patients and nurse educators. The specified change in how people perceive health management is especially important in the context of the present-day epidemic of coronavirus. Given the rapid spread of the epidemic and its recent transformation into the pandemic, reinforcing the instructions for people to remain safe is an essential task for APRNs and healthcare experts worldwide. In turn, the application of social media suits perfectly for the described purpose since it allows sharing information instantly and providing people with clear and concise guidelines for them to follow. Although social media mostly do not allow for detailed descriptions of specific health concerns and profound analysis of these issues, they serve their purpose of bulletin boards with clear and distinct guidelines that the members of the global community can apply to their daily routine.

Specifically, the use of positive messages in social media will reinforce the importance of guidelines and ensuring that people will follow them properly. For instance, Sumner et al. mention that the use of social networks has helped to promote social sharing. As the authors explain, “In topic areas such as cancer support, investigators found that the degree of positive sentiment in a message is associated with increased message spread” (Summer et al, p. 143). Therefore, the inclusion of positive thinking and positive emotions into the process of knowledge sharing enhances the extent to which people are willing to engage in the discussion. Moreover, the rise in the inclination to share a message that is positive will allow fighting some of the most severe health concerns that the global community is facing presently, primarily, the coronavirus.

Furthermore, the discussion sparked by the authors raises the question of inaccurate health-related information in social media and the means of filtering data. Indeed, for an uninitiated user of social media, discerning between accurate health-related information and the posts that reinforce health-associated myths is virtually impossible. Although some indicators such as the identity of the user posting the information could provide hints regarding the veracity of data, social media users have to rely on their intuition for the most part. Therefore, it is also critical for nurses to develop strategies for shielding social media users from the data that provides a distorted picture of health management.

Finally, the issue of addressing serious health concerns in social media should be discussed as a contentious subject. Given the outcomes of the research performed by Summer et al., it is critical to focus on delivering positive messages to target audiences to increase compliance with the established health management strategies. However, when tackling a serious health concern that has led or may potentially lead to a rapid rise in lethal outcomes, remaining positive becomes quite challenging. Not only will a message sound false in the specified circumstances, but it is also likely to be perceived in a negative light due to the dissonance between the subject matter and the tone of its delivery. Therefore, the outcomes of the study pose a difficult dilemma for educators and healthcare providers to resolve when addressing their target audiences via social media. Namely, retaining positivity while talking about serious issues is likely to become a major stumbling block for most healthcare service members.

The outcomes of the study performed by Summer et al. have offered a range of important insights, the significance of positivity in modern media as the means of encouraging citizens to accept healthy behaviors being one of the key conclusions. However, to apply the specified results to the management of current public health concerns, one will have to shape the existing framework for communicating with patients significantly. Therefore, the research should be seen as the basis for redesigning the present health education strategy, as well as the approach toward conversing with patients.

Sumner, Steven A., et al. “Factors Associated with Increased Dissemination of Positive Mental Health Messaging on Social Media.” Crisis: The Journal of Crisis Intervention and Suicide Prevention , vol. 41, no. 2, 2019, pp. 141-145. doi:10.1027/0227-5910/a000598.

• Violence and Facebook Posts Among Young People
• Psychology: Social Media and Bullying
• How Do Social Media Influencers Convey the Message of Body Positivity?
• The Way the Federal Government Treated Native Americans
• Social Darwinism Through the History
• Technological Determinism Perspective Discussion
• Cyberbullying as a Major Problem in Contemporary Society
• Self-Disclosure and Social Media
• Social Media Satirical Cartoon by M. Wuerker
• “Westside Today” and “Gazette Newspapers”: Comparative Characteristics
• Chicago (A-D)
• Chicago (N-B)

IvyPanda. (2022, February 17). Social Media and Mental Health. https://ivypanda.com/essays/social-media-and-mental-health/

"Social Media and Mental Health." IvyPanda , 17 Feb. 2022, ivypanda.com/essays/social-media-and-mental-health/.

IvyPanda . (2022) 'Social Media and Mental Health'. 17 February.

IvyPanda . 2022. "Social Media and Mental Health." February 17, 2022. https://ivypanda.com/essays/social-media-and-mental-health/.

1. IvyPanda . "Social Media and Mental Health." February 17, 2022. https://ivypanda.com/essays/social-media-and-mental-health/.

Bibliography

IvyPanda . "Social Media and Mental Health." February 17, 2022. https://ivypanda.com/essays/social-media-and-mental-health/.

• To find inspiration for your paper and overcome writer’s block
• As a source of information (ensure proper referencing)
• As a template for you assignment

• Open access
• Published: 06 July 2023

Pros & cons: impacts of social media on mental health

• Ágnes Zsila 1 , 2 &
• Marc Eric S. Reyes   ORCID: orcid.org/0000-0002-5280-1315 3  

BMC Psychology volume  11 , Article number:  201 ( 2023 ) Cite this article

596k Accesses

28 Citations

127 Altmetric

Metrics details

The use of social media significantly impacts mental health. It can enhance connection, increase self-esteem, and improve a sense of belonging. But it can also lead to tremendous stress, pressure to compare oneself to others, and increased sadness and isolation. Mindful use is essential to social media consumption.

Social media has become integral to our daily routines: we interact with family members and friends, accept invitations to public events, and join online communities to meet people who share similar preferences using these platforms. Social media has opened a new avenue for social experiences since the early 2000s, extending the possibilities for communication. According to recent research [ 1 ], people spend 2.3 h daily on social media. YouTube, TikTok, Instagram, and Snapchat have become increasingly popular among youth in 2022, and one-third think they spend too much time on these platforms [ 2 ]. The considerable time people spend on social media worldwide has directed researchers’ attention toward the potential benefits and risks. Research shows excessive use is mainly associated with lower psychological well-being [ 3 ]. However, findings also suggest that the quality rather than the quantity of social media use can determine whether the experience will enhance or deteriorate the user’s mental health [ 4 ]. In this collection, we will explore the impact of social media use on mental health by providing comprehensive research perspectives on positive and negative effects.

Social media can provide opportunities to enhance the mental health of users by facilitating social connections and peer support [ 5 ]. Indeed, online communities can provide a space for discussions regarding health conditions, adverse life events, or everyday challenges, which may decrease the sense of stigmatization and increase belongingness and perceived emotional support. Mutual friendships, rewarding social interactions, and humor on social media also reduced stress during the COVID-19 pandemic [ 4 ].

On the other hand, several studies have pointed out the potentially detrimental effects of social media use on mental health. Concerns have been raised that social media may lead to body image dissatisfaction [ 6 ], increase the risk of addiction and cyberbullying involvement [ 5 ], contribute to phubbing behaviors [ 7 ], and negatively affects mood [ 8 ]. Excessive use has increased loneliness, fear of missing out, and decreased subjective well-being and life satisfaction [ 8 ]. Users at risk of social media addiction often report depressive symptoms and lower self-esteem [ 9 ].

Overall, findings regarding the impact of social media on mental health pointed out some essential resources for psychological well-being through rewarding online social interactions. However, there is a need to raise awareness about the possible risks associated with excessive use, which can negatively affect mental health and everyday functioning [ 9 ]. There is neither a negative nor positive consensus regarding the effects of social media on people. However, by teaching people social media literacy, we can maximize their chances of having balanced, safe, and meaningful experiences on these platforms [ 10 ].

We encourage researchers to submit their research articles and contribute to a more differentiated overview of the impact of social media on mental health. BMC Psychology welcomes submissions to its new collection, which promises to present the latest findings in the emerging field of social media research. We seek research papers using qualitative and quantitative methods, focusing on social media users’ positive and negative aspects. We believe this collection will provide a more comprehensive picture of social media’s positive and negative effects on users’ mental health.

Data Availability

Not applicable.

Statista. (2022). Time spent on social media [Chart]. Accessed June 14, 2023, from https://www.statista.com/chart/18983/time-spent-on-social-media/ .

Pew Research Center. (2023). Teens and social media: Key findings from Pew Research Center surveys. Retrieved June 14, 2023, from https://www.pewresearch.org/short-reads/2023/04/24/teens-and-social-media-key-findings-from-pew-research-center-surveys/ .

Boer, M., Van Den Eijnden, R. J., Boniel-Nissim, M., Wong, S. L., Inchley, J. C.,Badura, P.,… Stevens, G. W. (2020). Adolescents’ intense and problematic social media use and their well-being in 29 countries. Journal of Adolescent Health , 66(6), S89-S99. https://doi.org/10.1016/j.jadohealth.2020.02.011.

Marciano L, Ostroumova M, Schulz PJ, Camerini AL. Digital media use and adolescents’ mental health during the COVID-19 pandemic: a systematic review and meta-analysis. Front Public Health. 2022;9:2208. https://doi.org/10.3389/fpubh.2021.641831 .

Article   Google Scholar  

Naslund JA, Bondre A, Torous J, Aschbrenner KA. Social media and mental health: benefits, risks, and opportunities for research and practice. J Technol Behav Sci. 2020;5:245–57. https://doi.org/10.1007/s41347-020-00094-8 .

Article   PubMed   PubMed Central   Google Scholar  

Harriger JA, Thompson JK, Tiggemann M. TikTok, TikTok, the time is now: future directions in social media and body image. Body Image. 2023;44:222–6. https://doi.org/10.1016/j.bodyim.2021.12.005 .

Article   PubMed   Google Scholar  

Chi LC, Tang TC, Tang E. The phubbing phenomenon: a cross-sectional study on the relationships among social media addiction, fear of missing out, personality traits, and phubbing behavior. Curr Psychol. 2022;41(2):1112–23. https://doi.org/10.1007/s12144-022-0135-4 .

Valkenburg PM. Social media use and well-being: what we know and what we need to know. Curr Opin Psychol. 2022;45:101294. https://doi.org/10.1016/j.copsyc.2020.101294 .

Bányai F, Zsila Á, Király O, Maraz A, Elekes Z, Griffiths MD, Urbán R, Farkas J, Rigó P Jr, Demetrovics Z. Problematic social media use: results from a large-scale nationally representative adolescent sample. PLoS ONE. 2017;12(1):e0169839. https://doi.org/10.1371/journal.pone.0169839 .

American Psychological Association. (2023). APA panel issues recommendations for adolescent social media use. Retrieved from https://apa-panel-issues-recommendations-for-adolescent-social-media-use-774560.html .

Download references

Acknowledgements

Ágnes Zsila was supported by the ÚNKP-22-4 New National Excellence Program of the Ministry for Culture and Innovation from the source of the National Research, Development and Innovation Fund.

Author information

Authors and affiliations.

Institute of Psychology, Pázmány Péter Catholic University, Budapest, Hungary

Ágnes Zsila

Institute of Psychology, ELTE Eötvös Loránd University, Budapest, Hungary

Department of Psychology, College of Science, University of Santo Tomas, Manila, 1008, Philippines

Marc Eric S. Reyes

You can also search for this author in PubMed   Google Scholar

Contributions

AZ conceived and drafted the Editorial. MESR wrote the abstract and revised the Editorial. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Marc Eric S. Reyes .

Ethics declarations

Ethics approval and consent to participate, consent for publication, competing interests.

The authors have no competing interests to declare relevant to the content of this article.

Additional information

Publisher’s note.

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

Rights and permissions

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

Reprints and permissions

About this article

Cite this article.

Zsila, Á., Reyes, M.E.S. Pros & cons: impacts of social media on mental health. BMC Psychol 11 , 201 (2023). https://doi.org/10.1186/s40359-023-01243-x

Download citation

Received : 15 June 2023

Accepted : 03 July 2023

Published : 06 July 2023

DOI : https://doi.org/10.1186/s40359-023-01243-x

Share this article

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

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

Provided by the Springer Nature SharedIt content-sharing initiative

• Social media
• Mental health

BMC Psychology

ISSN: 2050-7283

• General enquiries: [email protected]

Advertisement

Social Media and Mental Health: Benefits, Risks, and Opportunities for Research and Practice

• Published: 20 April 2020
• Volume 5 , pages 245–257, ( 2020 )

Cite this article

• John A. Naslund 1 ,
• Ameya Bondre 2 ,
• John Torous 3 &
• Kelly A. Aschbrenner 4  

462k Accesses

251 Citations

194 Altmetric

26 Mentions

Explore all metrics

Avoid common mistakes on your manuscript.

Introduction

Social media has become a prominent fixture in the lives of many individuals facing the challenges of mental illness. Social media refers broadly to web and mobile platforms that allow individuals to connect with others within a virtual network (such as Facebook, Twitter, Instagram, Snapchat, or LinkedIn), where they can share, co-create, or exchange various forms of digital content, including information, messages, photos, or videos (Ahmed et al. 2019 ). Studies have reported that individuals living with a range of mental disorders, including depression, psychotic disorders, or other severe mental illnesses, use social media platforms at comparable rates as the general population, with use ranging from about 70% among middle-age and older individuals to upwards of 97% among younger individuals (Aschbrenner et al. 2018b ; Birnbaum et al. 2017b ; Brunette et al. 2019 ; Naslund et al. 2016 ). Other exploratory studies have found that many of these individuals with mental illness appear to turn to social media to share their personal experiences, seek information about their mental health and treatment options, and give and receive support from others facing similar mental health challenges (Bucci et al. 2019 ; Naslund et al. 2016b ).

Across the USA and globally, very few people living with mental illness have access to adequate mental health services (Patel et al. 2018 ). The wide reach and near ubiquitous use of social media platforms may afford novel opportunities to address these shortfalls in existing mental health care, by enhancing the quality, availability, and reach of services. Recent studies have explored patterns of social media use, impact of social media use on mental health and wellbeing, and the potential to leverage the popularity and interactive features of social media to enhance the delivery of interventions. However, there remains uncertainty regarding the risks and potential harms of social media for mental health (Orben and Przybylski 2019 ) and how best to weigh these concerns against potential benefits.

In this commentary, we summarized current research on the use of social media among individuals with mental illness, with consideration of the impact of social media on mental wellbeing, as well as early efforts using social media for delivery of evidence-based programs for addressing mental health problems. We searched for recent peer reviewed publications in Medline and Google Scholar using the search terms “mental health” or “mental illness” and “social media,” and searched the reference lists of recent reviews and other relevant studies. We reviewed the risks, potential harms, and necessary safety precautions with using social media for mental health. Overall, our goal was to consider the role of social media as a potentially viable intervention platform for offering support to persons with mental disorders, promoting engagement and retention in care, and enhancing existing mental health services, while balancing the need for safety. Given this broad objective, we did not perform a systematic search of the literature and we did not apply specific inclusion criteria based on study design or type of mental disorder.

Social Media Use and Mental Health

In 2020, there are an estimated 3.8 billion social media users worldwide, representing half the global population (We Are Social 2020 ). Recent studies have shown that individuals with mental disorders are increasingly gaining access to and using mobile devices, such as smartphones (Firth et al. 2015 ; Glick et al. 2016 ; Torous et al. 2014a , b ). Similarly, there is mounting evidence showing high rates of social media use among individuals with mental disorders, including studies looking at engagement with these popular platforms across diverse settings and disorder types. Initial studies from 2015 found that nearly half of a sample of psychiatric patients were social media users, with greater use among younger individuals (Trefflich et al. 2015 ), while 47% of inpatients and outpatients with schizophrenia reported using social media, of which 79% reported at least once-a-week usage of social media websites (Miller et al. 2015 ). Rates of social media use among psychiatric populations have increased in recent years, as reflected in a study with data from 2017 showing comparable rates of social media use (approximately 70%) among individuals with serious mental illness in treatment as compared with low-income groups from the general population (Brunette et al. 2019 ).

Similarly, among individuals with serious mental illness receiving community-based mental health services, a recent study found equivalent rates of social media use as the general population, even exceeding 70% of participants (Naslund et al. 2016 ). Comparable findings were demonstrated among middle-age and older individuals with mental illness accessing services at peer support agencies, where 72% of respondents reported using social media (Aschbrenner et al. 2018b ). Similar results, with 68% of those with first episode psychosis using social media daily were reported in another study (Abdel-Baki et al. 2017 ).

Individuals who self-identified as having a schizophrenia spectrum disorder responded to a survey shared through the National Alliance of Mental Illness (NAMI) and reported that visiting social media sites was one of their most common activities when using digital devices, taking up roughly 2 h each day (Gay et al. 2016 ). For adolescents and young adults ages 12 to 21 with psychotic disorders and mood disorders, over 97% reported using social media, with average use exceeding 2.5 h per day (Birnbaum et al. 2017b ). Similarly, in a sample of adolescents ages 13–18 recruited from community mental health centers, 98% reported using social media, with YouTube as the most popular platform, followed by Instagram and Snapchat (Aschbrenner et al. 2019 ).

Research has also explored the motivations for using social media as well as the perceived benefits of interacting on these platforms among individuals with mental illness. In the sections that follow (see Table 1 for a summary), we consider three potentially unique features of interacting and connecting with others on social media that may offer benefits for individuals living with mental illness. These include: (1) Facilitate social interaction; (2) Access to a peer support network; and (3) Promote engagement and retention in services.

Facilitate Social Interaction

Social media platforms offer near continuous opportunities to connect and interact with others, regardless of time of day or geographic location. This on demand ease of communication may be especially important for facilitating social interaction among individuals with mental disorders experiencing difficulties interacting in face-to-face settings. For example, impaired social functioning is a common deficit in schizophrenia spectrum disorders, and social media may facilitate communication and interacting with others for these individuals (Torous and Keshavan 2016 ). This was suggested in one study where participants with schizophrenia indicated that social media helped them to interact and socialize more easily (Miller et al. 2015 ). Like other online communication, the ability to connect with others anonymously may be an important feature of social media, especially for individuals living with highly stigmatizing health conditions (Berger et al. 2005 ), such as serious mental disorders (Highton-Williamson et al. 2015 ).

Studies have found that individuals with serious mental disorders (Spinzy et al. 2012 ) as well as young adults with mental illness (Gowen et al. 2012 ) appear to form online relationships and connect with others on social media as often as social media users from the general population. This is an important observation because individuals living with serious mental disorders typically have few social contacts in the offline world and also experience high rates of loneliness (Badcock et al. 2015 ; Giacco et al. 2016 ). Among individuals receiving publicly funded mental health services who use social media, nearly half (47%) reported using these platforms at least weekly to feel less alone (Brusilovskiy et al. 2016 ). In another study of young adults with serious mental illness, most indicated that they used social media to help feel less isolated (Gowen et al. 2012 ). Interestingly, more frequent use of social media among a sample of individuals with serious mental illness was associated with greater community participation, measured as participation in shopping, work, religious activities, or visiting friends and family, as well as greater civic engagement, reflected as voting in local elections (Brusilovskiy et al. 2016 ).

Emerging research also shows that young people with moderate to severe depressive symptoms appear to prefer communicating on social media rather than in-person (Rideout and Fox 2018 ), while other studies have found that some individuals may prefer to seek help for mental health concerns online rather than through in-person encounters (Batterham and Calear 2017 ). In a qualitative study, participants with schizophrenia described greater anonymity, the ability to discover that other people have experienced similar health challenges and reducing fears through greater access to information as important motivations for using the Internet to seek mental health information (Schrank et al. 2010 ). Because social media does not require the immediate responses necessary in face-to-face communication, it may overcome deficits with social interaction due to psychotic symptoms that typically adversely affect face-to-face conversations (Docherty et al. 1996 ). Online social interactions may not require the use of non-verbal cues, particularly in the initial stages of interaction (Kiesler et al. 1984 ), with interactions being more fluid and within the control of users, thereby overcoming possible social anxieties linked to in-person interaction (Indian and Grieve 2014 ). Furthermore, many individuals with serious mental disorders can experience symptoms including passive social withdrawal, blunted affect, and attentional impairment, as well as active social avoidance due to hallucinations or other concerns (Hansen et al. 2009 ), thus potentially reinforcing the relative advantage, as perceived by users, of using social media over in person conversations.

Access to a Peer Support Network

There is growing recognition about the role that social media channels could play in enabling peer support (Bucci et al. 2019 ; Naslund et al. 2016b ), referred to as a system of mutual giving and receiving where individuals who have endured the difficulties of mental illness can offer hope, friendship, and support to others facing similar challenges (Davidson et al. 2006 ; Mead et al. 2001 ). Initial studies exploring use of online self-help forums among individuals with serious mental illnesses have found that individuals with schizophrenia appeared to use these forums for self-disclosure and sharing personal experiences, in addition to providing or requesting information, describing symptoms, or discussing medication (Haker et al. 2005 ), while users with bipolar disorder reported using these forums to ask for help from others about their illness (Vayreda and Antaki 2009 ). More recently, in a review of online social networking in people with psychosis, Highton-Williamson et al. ( 2015 ) highlight that an important purpose of such online connections was to establish new friendships, pursue romantic relationships, maintain existing relationships or reconnect with people, and seek online peer support from others with lived experience (Highton-Williamson et al. 2015 ).

Online peer support among individuals with mental illness has been further elaborated in various studies. In a content analysis of comments posted to YouTube by individuals who self-identified as having a serious mental illness, there appeared to be opportunities to feel less alone, provide hope, find support and learn through mutual reciprocity, and share coping strategies for day-to-day challenges of living with a mental illness (Naslund et al. 2014 ). In another study, Chang ( 2009 ) delineated various communication patterns in an online psychosis peer-support group (Chang 2009 ). Specifically, different forms of support emerged, including “informational support” about medication use or contacting mental health providers, “esteem support” involving positive comments for encouragement, “network support” for sharing similar experiences, and “emotional support” to express understanding of a peer’s situation and offer hope or confidence (Chang 2009 ). Bauer et al. ( 2013 ) reported that the main interest in online self-help forums for patients with bipolar disorder was to share emotions with others, allow exchange of information, and benefit by being part of an online social group (Bauer et al. 2013 ).

For individuals who openly discuss mental health problems on Twitter, a study by Berry et al. ( 2017 ) found that this served as an important opportunity to seek support and to hear about the experiences of others (Berry et al. 2017 ). In a survey of social media users with mental illness, respondents reported that sharing personal experiences about living with mental illness and opportunities to learn about strategies for coping with mental illness from others were important reasons for using social media (Naslund et al. 2017 ). A computational study of mental health awareness campaigns on Twitter provides further support with inspirational posts and tips being the most shared (Saha et al. 2019 ). Taken together, these studies offer insights about the potential for social media to facilitate access to an informal peer support network, though more research is necessary to examine how these online interactions may impact intentions to seek care, illness self-management, and clinically meaningful outcomes in offline contexts.

Promote Engagement and Retention in Services

Many individuals living with mental disorders have expressed interest in using social media platforms for seeking mental health information (Lal et al. 2018 ), connecting with mental health providers (Birnbaum et al. 2017b ), and accessing evidence-based mental health services delivered over social media specifically for coping with mental health symptoms or for promoting overall health and wellbeing (Naslund et al. 2017 ). With the widespread use of social media among individuals living with mental illness combined with the potential to facilitate social interaction and connect with supportive peers, as summarized above, it may be possible to leverage the popular features of social media to enhance existing mental health programs and services. A recent review by Biagianti et al. ( 2018 ) found that peer-to-peer support appeared to offer feasible and acceptable ways to augment digital mental health interventions for individuals with psychotic disorders by specifically improving engagement, compliance, and adherence to the interventions and may also improve perceived social support (Biagianti et al. 2018 ).

Among digital programs that have incorporated peer-to-peer social networking consistent with popular features on social media platforms, a pilot study of the HORYZONS online psychosocial intervention demonstrated significant reductions in depression among patients with first episode psychosis (Alvarez-Jimenez et al. 2013 ). Importantly, the majority of participants (95%) in this study engaged with the peer-to-peer networking feature of the program, with many reporting increases in perceived social connectedness and empowerment in their recovery process (Alvarez-Jimenez et al. 2013 ). This moderated online social therapy program is now being evaluated as part of a large randomized controlled trial for maintaining treatment effects from first episode psychosis services (Alvarez-Jimenez et al. 2019 ).

Other early efforts have demonstrated that use of digital environments with the interactive peer-to-peer features of social media can enhance social functioning and wellbeing in young people at high risk of psychosis (Alvarez-Jimenez et al. 2018 ). There has also been a recent emergence of several mobile apps to support symptom monitoring and relapse prevention in psychotic disorders. Among these apps, the development of PRIME (Personalized Real-time Intervention for Motivational Enhancement) has involved working closely with young people with schizophrenia to ensure that the design of the app has the look and feel of mainstream social media platforms, as opposed to existing clinical tools (Schlosser et al. 2016 ). This unique approach to the design of the app is aimed at promoting engagement and ensuring that the app can effectively improve motivation and functioning through goal setting and promoting better quality of life of users with schizophrenia (Schlosser et al. 2018 ).

Social media platforms could also be used to promote engagement and participation in in-person services delivered through community mental health settings. For example, the peer-based lifestyle intervention called PeerFIT targets weight loss and improved fitness among individuals living with serious mental illness through a combination of in-person lifestyle classes, exercise groups, and use of digital technologies (Aschbrenner et al. 2016b , c ). The intervention holds tremendous promise as lack of support is one of the largest barriers towards exercise in patients with serious mental illness (Firth et al. 2016 ), and it is now possible to use social media to counter such. Specifically, in PeerFIT, a private Facebook group is closely integrated into the program to offer a closed platform where participants can connect with the lifestyle coaches, access intervention content, and support or encourage each other as they work towards their lifestyle goals (Aschbrenner et al. 2016a ; Naslund et al. 2016a ). To date, this program has demonstrated preliminary effectiveness for meaningfully reducing cardiovascular risk factors that contribute to early mortality in this patient group (Aschbrenner, Naslund, Shevenell, Kinney, et al., 2016), while the Facebook component appears to have increased engagement in the program, while allowing participants who were unable to attend in-person sessions due to other health concerns or competing demands to remain connected with the program (Naslund et al. 2018 ). This lifestyle intervention is currently being evaluated in a randomized controlled trial enrolling young adults with serious mental illness from real world community mental health services settings (Aschbrenner et al. 2018a ).

These examples highlight the promise of incorporating the features of popular social media into existing programs, which may offer opportunities to safely promote engagement and program retention, while achieving improved clinical outcomes. This is an emerging area of research, as evidenced by several important effectiveness trials underway (Alvarez-Jimenez et al. 2019 ; Aschbrenner et al. 2018a ), including efforts to leverage online social networking to support family caregivers of individuals receiving first episode psychosis services (Gleeson et al. 2017 ).

Challenges with Social Media for Mental Health

The science on the role of social media for engaging persons with mental disorders needs a cautionary note on the effects of social media usage on mental health and wellbeing, particularly in adolescents and young adults. While the risks and harms of social media are frequently covered in the popular press and mainstream news reports, careful consideration of the research in this area is necessary. In a review of 43 studies in young people, many benefits of social media were cited, including increased self-esteem and opportunities for self-disclosure (Best et al. 2014 ). Yet, reported negative effects were an increased exposure to harm, social isolation, depressive symptoms, and bullying (Best et al. 2014 ). In the sections that follow (see Table 1 for a summary), we consider three major categories of risk related to use of social media and mental health. These include: (1) Impact on symptoms; (2) Facing hostile interactions; and (3) Consequences for daily life.

Impact on Symptoms

Studies consistently highlight that use of social media, especially heavy use and prolonged time spent on social media platforms, appears to contribute to increased risk for a variety of mental health symptoms and poor wellbeing, especially among young people (Andreassen et al. 2016 ; Kross et al. 2013 ; Woods and Scott 2016 ). This may partly be driven by the detrimental effects of screen time on mental health, including increased severity of anxiety and depressive symptoms, which have been well documented (Stiglic and Viner 2019 ). Recent studies have reported negative effects of social media use on mental health of young people, including social comparison pressure with others and greater feeling of social isolation after being rejected by others on social media (Rideout and Fox 2018 ). In a study of young adults, it was found that negative comparisons with others on Facebook contributed to risk of rumination and subsequent increases in depression symptoms (Feinstein et al. 2013 ). Still, the cross-sectional nature of many screen time and mental health studies makes it challenging to reach causal inferences (Orben and Przybylski 2019 ).

Quantity of social media use is also an important factor, as highlighted in a survey of young adults ages 19 to 32, where more frequent visits to social media platforms each week were correlated with greater depressive symptoms (Lin et al. 2016 ). More time spent using social media is also associated with greater symptoms of anxiety (Vannucci et al. 2017 ). The actual number of platforms accessed also appears to contribute to risk as reflected in another national survey of young adults where use of a large number of social media platforms was associated with negative impact on mental health (Primack et al. 2017 ). Among survey respondents using between 7 and 11 different social media platforms compared with respondents using only 2 or fewer platforms, there were 3 times greater odds of having high levels of depressive symptoms and a 3.2 times greater odds of having high levels of anxiety symptoms (Primack et al. 2017 ).

Many researchers have postulated that worsening mental health attributed to social media use may be because social media replaces face-to-face interactions for young people (Twenge and Campbell 2018 ) and may contribute to greater loneliness (Bucci et al. 2019 ) and negative effects on other aspects of health and wellbeing (Woods and Scott 2016 ). One nationally representative survey of US adolescents found that among respondents who reported more time accessing media such as social media platforms or smartphone devices, there were significantly greater depressive symptoms and increased risk of suicide when compared with adolescents who reported spending more time on non-screen activities, such as in-person social interaction or sports and recreation activities (Twenge et al. 2018 ). For individuals living with more severe mental illnesses, the effects of social media on psychiatric symptoms have received less attention. One study found that participation in chat rooms may contribute to worsening symptoms in young people with psychotic disorders (Mittal et al. 2007 ), while another study of patients with psychosis found that social media use appeared to predict low mood (Berry et al. 2018 ). These studies highlight a clear relationship between social media use and mental health that may not be present in general population studies (Orben and Przybylski 2019 ) and emphasize the need to explore how social media may contribute to symptom severity and whether protective factors may be identified to mitigate these risks.

Facing Hostile Interactions

Popular social media platforms can create potential situations where individuals may be victimized by negative comments or posts. Cyberbullying represents a form of online aggression directed towards specific individuals, such as peers or acquaintances, which is perceived to be most harmful when compared with random hostile comments posted online (Hamm et al. 2015 ). Importantly, cyberbullying on social media consistently shows harmful impact on mental health in the form of increased depressive symptoms as well as worsening of anxiety symptoms, as evidenced in a review of 36 studies among children and young people (Hamm et al. 2015 ). Furthermore, cyberbullying disproportionately impacts females as reflected in a national survey of adolescents in the USA, where females were twice as likely to be victims of cyberbullying compared with males (Alhajji et al. 2019 ). Most studies report cross-sectional associations between cyberbullying and symptoms of depression or anxiety (Hamm et al. 2015 ), though one longitudinal study in Switzerland found that cyberbullying contributed to significantly greater depression over time (Machmutow et al. 2012 ).

For youth ages 10 to 17 who reported major depressive symptomatology, there were over 3 times greater odds of facing online harassment in the last year compared with youth who reported mild or no depressive symptoms (Ybarra 2004 ). Similarly, in a 2018 national survey of young people, respondents ages 14 to 22 with moderate to severe depressive symptoms were more likely to have had negative experiences when using social media and, in particular, were more likely to report having faced hostile comments or being “trolled” from others when compared with respondents without depressive symptoms (31% vs. 14%) (Rideout and Fox 2018 ). As these studies depict risks for victimization on social media and the correlation with poor mental health, it is possible that individuals living with mental illness may also experience greater hostility online compared to individuals without mental illness. This would be consistent with research showing greater risk of hostility, including increased violence and discrimination, directed towards individuals living with mental illness in in-person contexts, especially targeted at those with severe mental illnesses (Goodman et al. 1999 ).

A computational study of mental health awareness campaigns on Twitter reported that while stigmatizing content was rare, it was actually the most spread (re-tweeted) demonstrating that harmful content can travel quickly on social media (Saha et al. 2019 ). Another study was able to map the spread of social media posts about the Blue Whale Challenge, an alleged game promoting suicide, over Twitter, YouTube, Reddit, Tumblr, and other forums across 127 countries (Sumner et al. 2019 ). These findings show that it is critical to monitor the actual content of social media posts, such as determining whether content is hostile or promotes harm to self or others. This is pertinent because existing research looking at duration of exposure cannot account for the impact of specific types of content on mental health and is insufficient to fully understand the effects of using these platforms on mental health.

Consequences for Daily Life

The ways in which individuals use social media can also impact their offline relationships and everyday activities. To date, reports have described risks of social media use pertaining to privacy, confidentiality, and unintended consequences of disclosing personal health information online (Torous and Keshavan 2016 ). Additionally, concerns have been raised about poor quality or misleading health information shared on social media and that social media users may not be aware of misleading information or conflicts of interest especially when the platforms promote popular content regardless of whether it is from a trustworthy source (Moorhead et al. 2013 ; Ventola 2014 ). For persons living with mental illness, there may be additional risks from using social media. A recent study that specifically explored the perspectives of social media users with serious mental illnesses, including participants with schizophrenia spectrum disorders, bipolar disorder, or major depression, found that over one third of participants expressed concerns about privacy when using social media (Naslund and Aschbrenner 2019 ). The reported risks of social media use were directly related to many aspects of everyday life, including concerns about threats to employment, fear of stigma and being judged, impact on personal relationships, and facing hostility or being hurt (Naslund and Aschbrenner 2019 ). While few studies have specifically explored the dangers of social media use from the perspectives of individuals living with mental illness, it is important to recognize that use of these platforms may contribute to risks that extend beyond worsening symptoms and that can affect different aspects of daily life.

In this commentary, we considered ways in which social media may yield benefits for individuals living with mental illness, while contrasting these with the possible harms. Studies reporting on the threats of social media for individuals with mental illness are mostly cross-sectional, making it difficult to draw conclusions about direction of causation. However, the risks are potentially serious. These risks should be carefully considered in discussions pertaining to use of social media and the broader use of digital mental health technologies, as avenues for mental health promotion or for supporting access to evidence-based programs or mental health services. At this point, it would be premature to view the benefits of social media as outweighing the possible harms, when it is clear from the studies summarized here that social media use can have negative effects on mental health symptoms, can potentially expose individuals to hurtful content and hostile interactions, and can result in serious consequences for daily life, including threats to employment and personal relationships. Despite these risks, it is also necessary to recognize that individuals with mental illness will continue to use social media given the ease of accessing these platforms and the immense popularity of online social networking. With this in mind, it may be ideal to raise awareness about these possible risks so that individuals can implement necessary safeguards, while highlighting that there could also be benefits. Being aware of the risks is an essential first step, before then recognizing that use of these popular platforms could contribute to some benefits like finding meaningful interactions with others, engaging with peer support networks, and accessing information and services.

To capitalize on the widespread use of social media and to achieve the promise that these platforms may hold for supporting the delivery of targeted mental health interventions, there is need for continued research to better understand how individuals living with mental illness use social media. Such efforts could inform safety measures and also encourage use of social media in ways that maximize potential benefits while minimizing risk of harm. It will be important to recognize how gender and race contribute to differences in use of social media for seeking mental health information or accessing interventions, as well as differences in how social media might impact mental wellbeing. For example, a national survey of 14- to 22-year olds in the USA found that female respondents were more likely to search online for information about depression or anxiety and to try to connect with other people online who share similar mental health concerns when compared with male respondents (Rideout and Fox 2018 ). In the same survey, there did not appear to be any differences between racial or ethnic groups in social media use for seeking mental health information (Rideout and Fox 2018 ). Social media use also appears to have a differential impact on mental health and emotional wellbeing between females and males (Booker et al. 2018 ), highlighting the need to explore unique experiences between gender groups to inform tailored programs and services. Research shows that lesbian, gay, bisexual, or transgender individuals frequently use social media for searching for health information and may be more likely compared with heterosexual individuals to share their own personal health experiences with others online (Rideout and Fox 2018 ). Less is known about use of social media for seeking support for mental health concerns among gender minorities, though this is an important area for further investigation as these individuals are more likely to experience mental health problems and online victimization when compared with heterosexual individuals (Mereish et al. 2019 ).

Similarly, efforts are needed to explore the relationship between social media use and mental health among ethnic and racial minorities. A recent study found that exposure to traumatic online content on social media showing violence or hateful posts directed at racial minorities contributed to increases in psychological distress, PTSD symptoms, and depression among African American and Latinx adolescents in the USA (Tynes et al. 2019 ). These concerns are contrasted by growing interest in the potential for new technologies including social media to expand the reach of services to underrepresented minority groups (Schueller et al. 2019 ). Therefore, greater attention is needed to understanding the perspectives of ethnic and racial minorities to inform effective and safe use of social media for mental health promotion efforts.

Research has found that individuals living with mental illness have expressed interest in accessing mental health services through social media platforms. A survey of social media users with mental illness found that most respondents were interested in accessing programs for mental health on social media targeting symptom management, health promotion, and support for communicating with health care providers and interacting with the health system (Naslund et al. 2017 ). Importantly, individuals with serious mental illness have also emphasized that any mental health intervention on social media would need to be moderated by someone with adequate training and credentials, would need to have ground rules and ways to promote safety and minimize risks, and importantly, would need to be free and easy to access.

An important strength with this commentary is that it combines a range of studies broadly covering the topic of social media and mental health. We have provided a summary of recent evidence in a rapidly advancing field with the goal of presenting unique ways that social media could offer benefits for individuals with mental illness, while also acknowledging the potentially serious risks and the need for further investigation. There are also several limitations with this commentary that warrant consideration. Importantly, as we aimed to address this broad objective, we did not conduct a systematic review of the literature. Therefore, the studies reported here are not exhaustive, and there may be additional relevant studies that were not included. Additionally, we only summarized published studies, and as a result, any reports from the private sector or websites from different organizations using social media or other apps containing social media–like features would have been omitted. Although, it is difficult to rigorously summarize work from the private sector, sometimes referred to as “gray literature,” because many of these projects are unpublished and are likely selective in their reporting of findings given the target audience may be shareholders or consumers.

Another notable limitation is that we did not assess risk of bias in the studies summarized in this commentary. We found many studies that highlighted risks associated with social media use for individuals living with mental illness; however, few studies of programs or interventions reported negative findings, suggesting the possibility that negative findings may go unpublished. This concern highlights the need for a future more rigorous review of the literature with careful consideration of bias and an accompanying quality assessment. Most of the studies that we described were from the USA, as well as from other higher income settings such as Australia or the UK. Despite the global reach of social media platforms, there is a dearth of research on the impact of these platforms on the mental health of individuals in diverse settings, as well as the ways in which social media could support mental health services in lower income countries where there is virtually no access to mental health providers. Future research is necessary to explore the opportunities and risks for social media to support mental health promotion in low-income and middle-income countries, especially as these countries face a disproportionate share of the global burden of mental disorders, yet account for the majority of social media users worldwide (Naslund et al. 2019 ).

Future Directions for Social Media and Mental Health

As we consider future research directions, the near ubiquitous social media use also yields new opportunities to study the onset and manifestation of mental health symptoms and illness severity earlier than traditional clinical assessments. There is an emerging field of research referred to as “digital phenotyping” aimed at capturing how individuals interact with their digital devices, including social media platforms, in order to study patterns of illness and identify optimal time points for intervention (Jain et al. 2015 ; Onnela and Rauch 2016 ). Given that most people access social media via mobile devices, digital phenotyping and social media are closely related (Torous et al. 2019 ). To date, the emergence of machine learning, a powerful computational method involving statistical and mathematical algorithms (Shatte et al. 2019 ), has made it possible to study large quantities of data captured from popular social media platforms such as Twitter or Instagram to illuminate various features of mental health (Manikonda and De Choudhury 2017 ; Reece et al. 2017 ). Specifically, conversations on Twitter have been analyzed to characterize the onset of depression (De Choudhury et al. 2013 ) as well as detecting users’ mood and affective states (De Choudhury et al. 2012 ), while photos posted to Instagram can yield insights for predicting depression (Reece and Danforth 2017 ). The intersection of social media and digital phenotyping will likely add new levels of context to social media use in the near future.

Several studies have also demonstrated that when compared with a control group, Twitter users with a self-disclosed diagnosis of schizophrenia show unique online communication patterns (Birnbaum et al. 2017a ), including more frequent discussion of tobacco use (Hswen et al. 2017 ), symptoms of depression and anxiety (Hswen et al. 2018b ), and suicide (Hswen et al. 2018a ). Another study found that online disclosures about mental illness appeared beneficial as reflected by fewer posts about symptoms following self-disclosure (Ernala et al. 2017 ). Each of these examples offers early insights into the potential to leverage widely available online data for better understanding the onset and course of mental illness. It is possible that social media data could be used to supplement additional digital data, such as continuous monitoring using smartphone apps or smart watches, to generate a more comprehensive “digital phenotype” to predict relapse and identify high-risk health behaviors among individuals living with mental illness (Torous et al. 2019 ).

With research increasingly showing the valuable insights that social media data can yield about mental health states, greater attention to the ethical concerns with using individual data in this way is necessary (Chancellor et al. 2019 ). For instance, data is typically captured from social media platforms without the consent or awareness of users (Bidargaddi et al. 2017 ), which is especially crucial when the data relates to a socially stigmatizing health condition such as mental illness (Guntuku et al. 2017 ). Precautions are needed to ensure that data is not made identifiable in ways that were not originally intended by the user who posted the content as this could place an individual at risk of harm or divulge sensitive health information (Webb et al. 2017 ; Williams et al. 2017 ). Promising approaches for minimizing these risks include supporting the participation of individuals with expertise in privacy, clinicians, and the target individuals with mental illness throughout the collection of data, development of predictive algorithms, and interpretation of findings (Chancellor et al. 2019 ).

In recognizing that many individuals living with mental illness use social media to search for information about their mental health, it is possible that they may also want to ask their clinicians about what they find online to check if the information is reliable and trustworthy. Alternatively, many individuals may feel embarrassed or reluctant to talk to their clinicians about using social media to find mental health information out of concerns of being judged or dismissed. Therefore, mental health clinicians may be ideally positioned to talk with their patients about using social media and offer recommendations to promote safe use of these sites while also respecting their patients’ autonomy and personal motivations for using these popular platforms. Given the gap in clinical knowledge about the impact of social media on mental health, clinicians should be aware of the many potential risks so that they can inform their patients while remaining open to the possibility that their patients may also experience benefits through use of these platforms. As awareness of these risks grows, it may be possible that new protections will be put in place by industry or through new policies that will make the social media environment safer. It is hard to estimate a number needed to treat or harm today given the nascent state of research, which means the patient and clinician need to weigh the choice on a personal level. Thus, offering education and information is an important first step in that process. As patients increasingly show interest in accessing mental health information or services through social media, it will be necessary for health systems to recognize social media as a potential avenue for reaching or offering support to patients. This aligns with growing emphasis on the need for greater integration of digital psychiatry, including apps, smartphones, or wearable devices, into patient care and clinical services through institution-wide initiatives and training clinical providers (Hilty et al. 2019 ). Within a learning healthcare environment where research and care are tightly intertwined and feedback between both is rapid, the integration of digital technologies into services may create new opportunities for advancing use of social media for mental health.

As highlighted in this commentary, social media has become an important part of the lives of many individuals living with mental disorders. Many of these individuals use social media to share their lived experiences with mental illness, to seek support from others, and to search for information about treatment recommendations, accessing mental health services and coping with symptoms (Bucci et al. 2019 ; Highton-Williamson et al. 2015 ; Naslund et al. 2016b ). As the field of digital mental health advances, the wide reach, ease of access, and popularity of social media platforms could be used to allow individuals in need of mental health services or facing challenges of mental illness to access evidence-based treatment and support. To achieve this end and to explore whether social media platforms can advance efforts to close the gap in available mental health services in the USA and globally, it will be essential for researchers to work closely with clinicians and with those affected by mental illness to ensure that possible benefits of using social media are carefully weighed against anticipated risks.

Abdel-Baki, A., Lal, S., Charron, D.-C., Stip, E., & Kara, N. (2017). Understanding access and use of technology among youth with first-episode psychosis to inform the development of technology-enabled therapeutic interventions. Early Intervention in Psychiatry, 11 (1), 72–76.

PubMed   Google Scholar  

Ahmed, Y. A., Ahmad, M. N., Ahmad, N., & Zakaria, N. H. (2019). Social media for knowledge-sharing: a systematic literature review. Telematics and Informatics, 37 , 72–112.

Google Scholar  

Alhajji, M., Bass, S., & Dai, T. (2019). Cyberbullying, mental health, and violence in adolescents and associations with sex and race: data from the 2015 youth risk behavior survey. Global Pediatric Health, 6 , 2333794X19868887.

PubMed   PubMed Central   Google Scholar  

Alvarez-Jimenez, M., Bendall, S., Lederman, R., Wadley, G., Chinnery, G., Vargas, S., Larkin, M., Killackey, E., McGorry, P., & Gleeson, J. F. (2013). On the HORYZON: moderated online social therapy for long-term recovery in first episode psychosis. Schizophrenia Research, 143 (1), 143–149.

Alvarez-Jimenez, M., Gleeson, J., Bendall, S., Penn, D., Yung, A., Ryan, R., et al. (2018). Enhancing social functioning in young people at ultra high risk (UHR) for psychosis: a pilot study of a novel strengths and mindfulness-based online social therapy. Schizophrenia Research, 202 , 369–377.

Alvarez-Jimenez, M., Bendall, S., Koval, P., Rice, S., Cagliarini, D., Valentine, L., et al. (2019). HORYZONS trial: protocol for a randomised controlled trial of a moderated online social therapy to maintain treatment effects from first-episode psychosis services. BMJ Open, 9 (2), e024104.

Andreassen, C. S., Billieux, J., Griffiths, M. D., Kuss, D. J., Demetrovics, Z., Mazzoni, E., & Pallesen, S. (2016). The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: a large-scale cross-sectional study. Psychology of Addictive Behaviors, 30 (2), 252.

Aschbrenner, K. A., Naslund, J. A., & Bartels, S. J. (2016a). A mixed methods study of peer-to-peer support in a group-based lifestyle intervention for adults with serious mental illness. Psychiatric Rehabilitation Journal, 39 (4), 328–334.

Aschbrenner, K. A., Naslund, J. A., Shevenell, M., Kinney, E., & Bartels, S. J. (2016b). A pilot study of a peer-group lifestyle intervention enhanced with mHealth technology and social media for adults with serious mental illness. The Journal of Nervous and Mental Disease, 204 (6), 483–486.

Aschbrenner, K. A., Naslund, J. A., Shevenell, M., Mueser, K. T., & Bartels, S. J. (2016c). Feasibility of behavioral weight loss treatment enhanced with peer support and mobile health technology for individuals with serious mental illness. Psychiatric Quarterly, 87 (3), 401–415.

Aschbrenner, K. A., Naslund, J. A., Gorin, A. A., Mueser, K. T., Scherer, E. A., Viron, M., et al. (2018a). Peer support and mobile health technology targeting obesity-related cardiovascular risk in young adults with serious mental illness: protocol for a randomized controlled trial. Contemporary Clinical Trials, 74 , 97–106.

Aschbrenner, K. A., Naslund, J. A., Grinley, T., Bienvenida, J. C. M., Bartels, S. J., & Brunette, M. (2018b). A survey of online and mobile technology use at peer support agencies. Psychiatric Quarterly , 1–10.

Aschbrenner, K. A., Naslund, J. A., Tomlinson, E. F., Kinney, A., Pratt, S. I., & Brunette, M. F. (2019). Adolescents’ use of digital technologies and preferences for mobile health coaching in mental health settings. Frontiers in Public Health. 7 , 178.

Badcock, J. C., Shah, S., Mackinnon, A., Stain, H. J., Galletly, C., Jablensky, A., & Morgan, V. A. (2015). Loneliness in psychotic disorders and its association with cognitive function and symptom profile. Schizophrenia Research, 169 (1–3), 268–273.

Batterham, P. J., & Calear, A. J. (2017). Preferences for internet-based mental health interventions in an adult online sample: findings from ann online community survey. JMIR Mental Health, 4 (2), e26.

Bauer, R., Bauer, M., Spiessl, H., & Kagerbauer, T. (2013). Cyber-support: an analysis of online self-help forums (online self-help forums in bipolar disorder). Nordic Journal of Psychiatry, 67 (3), 185–190.

Berger, M., Wagner, T. H., & Baker, L. C. (2005). Internet use and stigmatized illness. Social Science & Medicine, 61 (8), 1821–1827.

Berry, N., Lobban, F., Belousov, M., Emsley, R., Nenadic, G., & Bucci, S. (2017). # WhyWeTweetMH: understanding why people use Twitter to discuss mental health problems. Journal of Medical Internet Research, 19 (4), e107.

Berry, N., Emsley, R., Lobban, F., & Bucci, S. (2018). Social media and its relationship with mood, self-esteem and paranoia in psychosis. Acta Psychiatrica Scandinavica, 138 , 558–570.

Best, P., Manktelow, R., & Taylor, B. (2014). Online communication, social media and adolescent wellbeing: a systematic narrative review. Children and Youth Services Review, 41 , 27–36.

Biagianti, B., Quraishi, S. H., & Schlosser, D. A. (2018). Potential benefits of incorporating peer-to-peer interactions into digital interventions for psychotic disorders: a systematic review. Psychiatric Services, 69 (4), 377–388.

Bidargaddi, N., Musiat, P., Makinen, V.-P., Ermes, M., Schrader, G., & Licinio, J. (2017). Digital footprints: facilitating large-scale environmental psychiatric research in naturalistic settings through data from everyday technologies. Molecular Psychiatry, 22 (2), 164.

Birnbaum, M. L., Ernala, S. K., Rizvi, A. F., De Choudhury, M., & Kane, J. M. (2017a). A collaborative approach to identifying social media markers of schizophrenia by employing machine learning and clinical appraisals. Journal of Medical Internet Research, 19 (8), e289.

Birnbaum, M. L., Rizvi, A. F., Correll, C. U., Kane, J. M., & Confino, J. (2017b). Role of social media and the Internet in pathways to care for adolescents and young adults with psychotic disorders and non-psychotic mood disorders. Early Intervention in Psychiatry, 11 (4), 290–295.

Booker, C. L., Kelly, Y. J., & Sacker, A. (2018). Gender differences in the associations between age trends of social media interaction and well-being among 10-15 year olds in the UK. BMC Public Health, 18 (1), 321.

Brunette, M., Achtyes, E., Pratt, S., Stilwell, K., Opperman, M., Guarino, S., & Kay-Lambkin, F. (2019). Use of smartphones, computers and social media among people with SMI: opportunity for intervention. Community Mental Health Journal , 1–6.

Brusilovskiy, E., Townley, G., Snethen, G., & Salzer, M. S. (2016). Social media use, community participation and psychological well-being among individuals with serious mental illnesses. Computers in Human Behavior, 65 , 232–240.

Bucci, S., Schwannauer, M., & Berry, N. (2019). The digital revolution and its impact on mental health care. Psychology and Psychotherapy: Theory, Research and Practice, 92 (2), 277–297.

Chancellor, S., Birnbaum, M. L., Caine, E. D., Silenzio, V. M., & De Choudhury, M. (2019). A taxonomy of ethical tensions in inferring mental health states from social media. In Proceedings of the Conference on Fairness, Accountability, and Transparency, 79–88.

Chang, H. J. (2009). Online supportive interactions: using a network approach to examine communication patterns within a psychosis social support group in Taiwan. Journal of the American Society for Information Science and Technology, 60 (7), 1504–1517.

Davidson, L., Chinman, M., Sells, D., & Rowe, M. (2006). Peer support among adults with serious mental illness: a report from the field. Schizophrenia Bulletin, 32 (3), 443–450.

De Choudhury, M., Gamon, M., & Counts, S. (2012). Happy, nervous or surprised? classification of human affective states in social media. Paper presented at the sixth international Association for Advancement of Artificial Intelligence (AAAI) Conference on Weblogs and Social Meedia, 435–438.

De Choudhury, M., Gamon, M., Counts, S., & Horvitz, E. (2013). Predicting depression via social media. Paper presented at the seventh international Association for Advancement of Artificial Intelligence (AAAI) Conference on Weblogs and Social Media, 128–137.

Docherty, N. M., Hawkins, K. A., Hoffman, R. E., Quinlan, D. M., Rakfeldt, J., & Sledge, W. H. (1996). Working memory, attention, and communication disturbances in schizophrenia. Journal of Abnormal Psychology, 105 (2), 212–219.

Ernala, S. K., Rizvi, A. F., Birnbaum, M. L., Kane, J. M., & De Choudhury, M. (2017). Linguistic markers indicating therapeutic outcomes of social media disclosures of schizophrenia. Proceedings of the ACM on Human-Computer Interaction, 1 (1), 43.

Feinstein, B. A., Hershenberg, R., Bhatia, V., Latack, J. A., Meuwly, N., & Davila, J. (2013). Negative social comparison on Facebook and depressive symptoms: rumination as a mechanism. Psychology of Popular Media Culture, 2 (3), 161.

Firth, J., Cotter, J., Torous, J., Bucci, S., Firth, J. A., & Yung, A. R. (2015). Mobile phone ownership and endorsement of “mHealth” among people with psychosis: a meta-analysis of cross-sectional studies. Schizophrenia Bulletin, 42 (2), 448–455.

Firth, J., Rosenbaum, S., Stubbs, B., Gorczynski, P., Yung, A. R., & Vancampfort, D. (2016). Motivating factors and barriers towards exercise in severe mental illness: a systematic review and meta-analysis. Psychological Medicine, 46 (14), 2869–2881.

Gay, K., Torous, J., Joseph, A., Pandya, A., & Duckworth, K. (2016). Digital technology use among individuals with schizophrenia: results of an online survey. JMIR Mental Health, 3 (2), e15.

Giacco, D., Palumbo, C., Strappelli, N., Catapano, F., & Priebe, S. (2016). Social contacts and loneliness in people with psychotic and mood disorders. Comprehensive Psychiatry, 66 , 59–66.

Gleeson, J., Lederman, R., Herrman, H., Koval, P., Eleftheriadis, D., Bendall, S., Cotton, S. M., & Alvarez-Jimenez, M. (2017). Moderated online social therapy for carers of young people recovering from first-episode psychosis: study protocol for a randomised controlled trial. Trials, 18 (1), 27.

Glick, G., Druss, B., Pina, J., Lally, C., & Conde, M. (2016). Use of mobile technology in a community mental health setting. Journal of Telemedicine and Telecare, 22 (7), 430–435.

Goodman, L. A., Thompson, K. M., Weinfurt, K., Corl, S., Acker, P., Mueser, K. T., & Rosenberg, S. D. (1999). Reliability of reports of violent victimization and posttraumatic stress disorder among men and women with serious mental illness. Journal of Traumatic Stress: Official Publication of the International Society for Traumatic Stress Studies, 12 (4), 587–599.

Gowen, K., Deschaine, M., Gruttadara, D., & Markey, D. (2012). Young adults with mental health conditions and social networking websites: seeking tools to build community. Psychiatric Rehabilitation Journal, 35 (3), 245–250.

Guntuku, S. C., Yaden, D. B., Kern, M. L., Ungar, L. H., & Eichstaedt, J. C. (2017). Detecting depression and mental illness on social media: an integrative review. Current Opinion in Behavioral Sciences, 18 , 43–49.

Haker, H., Lauber, C., & Rössler, W. (2005). Internet forums: a self-help approach for individuals with schizophrenia? Acta Psychiatrica Scandinavica, 112 (6), 474–477.

Hamm, M. P., Newton, A. S., Chisholm, A., Shulhan, J., Milne, A., Sundar, P., Ennis, H., Scott, S. D., & Hartling, L. (2015). Prevalence and effect of cyberbullying on children and young people: a scoping review of social media studies. JAMA Pediatrics, 169 (8), 770–777.

Hansen, C. F., Torgalsbøen, A.-K., Melle, I., & Bell, M. D. (2009). Passive/apathetic social withdrawal and active social avoidance in schizophrenia: difference in underlying psychological processes. The Journal of Nervous and Mental Disease, 197 (4), 274–277.

Highton-Williamson, E., Priebe, S., & Giacco, D. (2015). Online social networking in people with psychosis: a systematic review. International Journal of Social Psychiatry, 61 (1), 92–101.

Hilty, D. M., Chan, S., Torous, J., Luo, J., & Boland, R. J. (2019). Mobile health, smartphone/device, and apps for psychiatry and medicine: competencies, training, and faculty development issues. Psychiatric Clinics, 42 (3), 513–534.

Hswen, Y., Naslund, J. A., Chandrashekar, P., Siegel, R., Brownstein, J. S., & Hawkins, J. B. (2017). Exploring online communication about cigarette smoking among Twitter users who self-identify as having schizophrenia. Psychiatry Research, 257 , 479–484.

Hswen, Y., Naslund, J. A., Brownstein, J. S., & Hawkins, J. B. (2018a). Monitoring online discussions about suicide among Twitter users with schizophrenia: exploratory study. JMIR Mental Health, 5 (4), e11483.

Hswen, Y., Naslund, J. A., Brownstein, J. S., & Hawkins, J. B. (2018b). Online communication about depression and anxiety among twitter users with schizophrenia: preliminary findings to inform a digital phenotype using social media. Psychiatric Quarterly, 89 (3), 569–580.

Indian, M., & Grieve, R. (2014). When Facebook is easier than face-to-face: social support derived from Facebook in socially anxious individuals. Personality and Individual Differences, 59 , 102–106.

Jain, S. H., Powers, B. W., Hawkins, J. B., & Brownstein, J. S. (2015). The digital phenotype. Nature Biotechnology, 33 (5), 462–463.

Kiesler, S., Siegel, J., & McGuire, T. W. (1984). Social psychological aspects of computer-mediated communication. American Psychologist, 39 , 1123–1134.

Kross, E., Verduyn, P., Demiralp, E., Park, J., Lee, D. S., Lin, N., Shablack, H., Jonides, J., & Ybarra, O. (2013). Facebook use predicts declines in subjective well-being in young adults. PLoS One, 8 (8), e69841.

Lal, S., Nguyen, V., & Theriault, J. (2018). Seeking mental health information and support online: experiences and perspectives of young people receiving treatment for first-episode psychosis. Early Intervention in Psychiatry, 12 (3), 324–330.

Lin, L. Y., Sidani, J. E., Shensa, A., Radovic, A., Miller, E., Colditz, J. B., Hoffman, B. L., Giles, L. M., & Primack, B. A. (2016). Association between social media use and depression among US young adults. Depression and Anxiety, 33 (4), 323–331.

Machmutow, K., Perren, S., Sticca, F., & Alsaker, F. D. (2012). Peer victimisation and depressive symptoms: can specific coping strategies buffer the negative impact of cybervictimisation? Emotional and Behavioural Difficulties, 17 (3–4), 403–420.

Manikonda, L., & De Choudhury, M. (2017). Modeling and understanding visual attributes of mental health disclosures in social media. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, 170–181.

Mead, S., Hilton, D., & Curtis, L. (2001). Peer support: a theoretical perspective. Psychiatric Rehabilitation Journal, 25 (2), 134–141.

Mereish, E. H., Sheskier, M., Hawthorne, D. J., & Goldbach, J. T. (2019). Sexual orientation disparities in mental health and substance use among Black American young people in the USA: effects of cyber and bias-based victimisation. Culture, Health & Sexuality, 21 (9), 985–998.

Miller, B. J., Stewart, A., Schrimsher, J., Peeples, D., & Buckley, P. F. (2015). How connected are people with schizophrenia? Cell phone, computer, email, and social media use. Psychiatry Research, 225 (3), 458–463.

Mittal, V. A., Tessner, K. D., & Walker, E. F. (2007). Elevated social Internet use and schizotypal personality disorder in adolescents. Schizophrenia Research, 94 (1–3), 50–57.

Moorhead, S. A., Hazlett, D. E., Harrison, L., Carroll, J. K., Irwin, A., & Hoving, C. (2013). A new dimension of health care: systematic review of the uses, benefits, and limitations of social media for health communication. Journal of Medical Internet Research, 15 (4), e85.

Naslund, J. A., & Aschbrenner, K. A. (2019). Risks to privacy with use of social media: understanding the views of social media users with serious mental illness. Psychiatric Services, 70 (7), 561–568.

Naslund, J. A., Grande, S. W., Aschbrenner, K. A., & Elwyn, G. (2014). Naturally occurring peer support through social media: the experiences of individuals with severe mental illness using YouTube. PLoS One, 9 (10), e110171.

Naslund, J. A., Aschbrenner, K. A., & Bartels, S. J. (2016). How people living with serious mental illness use smartphones, mobile apps, and social media. Psychiatric Rehabilitation Journal, 39 (4), 364–367.

Naslund, J. A., Aschbrenner, K. A., Marsch, L. A., & Bartels, S. J. (2016a). Feasibility and acceptability of Facebook for health promotion among people with serious mental illness. Digital Health, 2 , 2055207616654822.

PubMed Central   Google Scholar  

Naslund, J. A., Aschbrenner, K. A., Marsch, L. A., & Bartels, S. J. (2016b). The future of mental health care: peer-to-peer support and social media. Epidemiology and Psychiatric Sciences, 25 (2), 113–122.

Naslund, J. A., Aschbrenner, K. A., McHugo, G. J., Unützer, J., Marsch, L. A., & Bartels, S. J. (2019). Exploring opportunities to support mental health care using social media: A survey of social media users with mental illness. Early Intervention in Psychiatry, 13 (3), 405–413.

Naslund, J. A., Aschbrenner, K. A., Marsch, L. A., McHugo, G. J., & Bartels, S. J. (2018). Facebook for supporting a lifestyle intervention for people with major depressive disorder, bipolar disorder, and schizophrenia: an exploratory study. Psychiatric Quarterly, 89 (1), 81–94.

Naslund, J. A., Gonsalves, P. P., Gruebner, O., Pendse, S. R., Smith, S. L., Sharma, A., & Raviola, G. (2019). Digital innovations for global mental health: opportunities for data science, task sharing, and early intervention. Current Treatment Options in Psychiatry , 1–15.

Onnela, J.-P., & Rauch, S. L. (2016). Harnessing smartphone-based digital phenotyping to enhance behavioral and mental health. Neuropsychopharmacology, 41 (7), 1691–1696.

Orben, A., & Przybylski, A. K. (2019). The association between adolescent well-being and digital technology use. Nature Human Behaviour, 3 (2), 173–182.

Patel, V., Saxena, S., Lund, C., Thornicroft, G., Baingana, F., Bolton, P., et al. (2018). The Lancet Commission on global mental health and sustainable development. The Lancet, 392 (10157), 1553–1598.

Primack, B. A., Shensa, A., Escobar-Viera, C. G., Barrett, E. L., Sidani, J. E., Colditz, J. B., & James, A. E. (2017). Use of multiple social media platforms and symptoms of depression and anxiety: a nationally-representative study among US young adults. Computers in Human Behavior, 69 , 1–9.

Reece, A. G., & Danforth, C. M. (2017). Instagram photos reveal predictive markers of depression. EPJ Data Science, 6 (1), 15.

Reece, A. G., Reagan, A. J., Lix, K. L., Dodds, P. S., Danforth, C. M., & Langer, E. J. (2017). Forecasting the onset and course of mental illness with Twitter data. Scientific Reports, 7 (1), 13006.

Rideout, V., & Fox, S. (2018). Digital health practices, social media use, and mental well-being among teens and young adults in the U.S. Retrieved from San Francisco, CA: https://www.hopelab.org/reports/pdf/a-national-survey-by-hopelab-and-well-being-trust-2018.pdf . Accessed 10 Jan 2020.

Saha, K., Torous, J., Ernala, S. K., Rizuto, C., Stafford, A., & De Choudhury, M. (2019). A computational study of mental health awareness campaigns on social media. Translational behavioral medicine, 9 (6), 1197–1207.

Schlosser, D. A., Campellone, T., Kim, D., Truong, B., Vergani, S., Ward, C., & Vinogradov, S. (2016). Feasibility of PRIME: a cognitive neuroscience-informed mobile app intervention to enhance motivated behavior and improve quality of life in recent onset schizophrenia. JMIR Research Protocols, 5 (2).

Schlosser, D. A., Campellone, T. R., Truong, B., Etter, K., Vergani, S., Komaiko, K., & Vinogradov, S. (2018). Efficacy of PRIME, a mobile app intervention designed to improve motivation in young people with schizophrenia. Schizophrenia Bulletin, 44 (5), 1010–1020.

Schrank, B., Sibitz, I., Unger, A., & Amering, M. (2010). How patients with schizophrenia use the internet: qualitative study. Journal of Medical Internet Research, 12 (5), e70.

Schueller, S. M., Hunter, J. F., Figueroa, C., & Aguilera, A. (2019). Use of digital mental health for marginalized and underserved populations. Current Treatment Options in Psychiatry, 6 (3), 243–255.

Shatte, A. B., Hutchinson, D. M., & Teague, S. J. (2019). Machine learning in mental health: a scoping review of methods and applications. Psychological Medicine, 49 (9), 1426–1448.

Spinzy, Y., Nitzan, U., Becker, G., Bloch, Y., & Fennig, S. (2012). Does the Internet offer social opportunities for individuals with schizophrenia? A cross-sectional pilot study. Psychiatry Research, 198 (2), 319–320.

Stiglic, N., & Viner, R. M. (2019). Effects of screentime on the health and well-being of children and adolescents: a systematic review of reviews. BMJ Open, 9 (1), e023191.

Sumner, S. A., Galik, S., Mathieu, J., Ward, M., Kiley, T., Bartholow, B., et al. (2019). Temporal and geographic patterns of social media posts about an emerging suicide game. Journal of Adolescent Health, 65 (1), 94–100.

Torous, J., & Keshavan, M. (2016). The role of social media in schizophrenia: evaluating risks, benefits, and potential. Current Opinion in Psychiatry, 29 (3), 190–195.

Torous, J., Chan, S. R., Tan, S. Y.-M., Behrens, J., Mathew, I., Conrad, E. J., et al. (2014a). Patient smartphone ownership and interest in mobile apps to monitor symptoms of mental health conditions: a survey in four geographically distinct psychiatric clinics. JMIR Mental Health, 1 (1), e5.

Torous, J., Friedman, R., & Keshavan, M. (2014b). Smartphone ownership and interest in mobile applications to monitor symptoms of mental health conditions. JMIR mHealth and uHealth, 2 (1), e2.

Torous, J., Wisniewski, H., Bird, B., Carpenter, E., David, G., Elejalde, E., et al. (2019). Creating a digital health smartphone app and digital phenotyping platform for mental health and diverse healthcare needs: an interdisciplinary and collaborative approach. Journal of Technology in Behavioral Science, 4 (2), 73–85.

Trefflich, F., Kalckreuth, S., Mergl, R., & Rummel-Kluge, C. (2015). Psychiatric patients' internet use corresponds to the internet use of the general public. Psychiatry Research, 226 , 136–141.

Twenge, J. M., & Campbell, W. K. (2018). Associations between screen time and lower psychological well-being among children and adolescents: evidence from a population-based study. Preventive Medicine Reports, 12 , 271–283.

Twenge, J. M., Joiner, T. E., Rogers, M. L., & Martin, G. N. (2018). Increases in depressive symptoms, suicide-related outcomes, and suicide rates among US adolescents after 2010 and links to increased new media screen time. Clinical Psychological Science, 6 (1), 3–17.

Tynes, B. M., Willis, H. A., Stewart, A. M., & Hamilton, M. W. (2019). Race-related traumatic events online and mental health among adolescents of color. Journal of Adolescent Health, 65 (3), 371–377.

Vannucci, A., Flannery, K. M., & Ohannessian, C. M. (2017). Social media use and anxiety in emerging adults. Journal of Affective Disorders, 207 , 163–166.

Vayreda, A., & Antaki, C. (2009). Social support and unsolicited advice in a bipolar disorder online forum. Qualitative Health Research, 19 (7), 931–942.

Ventola, C. L. (2014). Social media and health care professionals: benefits, risks, and best practices. Pharmacy and Therapeutics, 39 (7), 491–520.

We Are Social. (2020). Digital in 2020. Retrieved from https://wearesocial.com/global-digital-report-2019 . Accessed 10 Jan 2020.

Webb, H., Jirotka, M., Stahl, B. C., Housley, W., Edwards, A., Williams, M., ... & Burnap, P. (2017). The ethical challenges of publishing Twitter data for research dissemination . Paper presented at the proceedings of the 2017 ACM on Web Science Conference, 339–348.

Williams, M. L., Burnap, P., & Sloan, L. (2017). Towards an ethical framework for publishing twitter data in social research: taking into account users’ views, online context and algorithmic estimation. Sociology, 51 (6), 1149–1168.

Woods, H. C., & Scott, H. (2016). # Sleepyteens: social media use in adolescence is associated with poor sleep quality, anxiety, depression and low self-esteem. Journal of Adolescence, 51 , 41–49.

Ybarra, M. L. (2004). Linkages between depressive symptomatology and internet harassment among young regular Internet users. Cyberpsychology & Behavior, 7 (2), 247–257.

Download references

Dr. Naslund is supported by a grant from the National Institute of Mental Health (U19MH113211). Dr. Aschbrenner is supported by a grant from the National Institute of Mental Health (1R01MH110965-01).

Author information

Authors and affiliations.

Department of Global Health and Social Medicine, Harvard Medical School, 641 Huntington Avenue, Boston, MA, 02115, USA

John A. Naslund

Digital Mental Health Research Consultant, Mumbai, India

Ameya Bondre

Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA

John Torous

Department of Psychiatry, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA

Kelly A. Aschbrenner

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to John A. Naslund .

Ethics declarations

Conflict of interest.

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note.

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

Rights and permissions

Reprints and permissions

About this article

Naslund, J.A., Bondre, A., Torous, J. et al. Social Media and Mental Health: Benefits, Risks, and Opportunities for Research and Practice. J. technol. behav. sci. 5 , 245–257 (2020). https://doi.org/10.1007/s41347-020-00134-x

Download citation

Received : 19 October 2019

Revised : 24 February 2020

Accepted : 17 March 2020

Published : 20 April 2020

Issue Date : September 2020

DOI : https://doi.org/10.1007/s41347-020-00134-x

Share this article

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

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

Provided by the Springer Nature SharedIt content-sharing initiative

• Find a journal
• Publish with us
• Track your research

Home — Essay Samples — Business — Media — The Impact of Social Media on Mental Health [600 Words]

The Impact of Social Media on Mental Health [600 Words]

• Categories: Media

About this sample

Words: 607 |

Published: Jun 13, 2024

Words: 607 | Page: 1 | 4 min read

Table of contents

Introduction, body paragraph.

Cite this Essay

To export a reference to this article please select a referencing style below:

Let us write you an essay from scratch

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

Get high-quality help

Prof Ernest (PhD)

Verified writer

• Expert in: Business

+ 120 experts online

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

No need to pay just yet!

Related Essays

6 pages / 2653 words

1 pages / 425 words

4 pages / 1651 words

3 pages / 1258 words

Remember! This is just a sample.

You can get your custom paper by one of our expert writers.

121 writers online

Still can’t find what you need?

Browse our vast selection of original essay samples, each expertly formatted and styled

Related Essays on Media

Bulmer, J. G., McLeod, J. M., & Rice, R. E. (2009). Television and Political Life: Studies in Six European Countries. Springer Science & Business Media.Fulcher, J., & Scott, J. (2011). Sociology. Oxford University Press.Kitts, [...]

In Chapter 8 of Thirteen American Arguments, the author Howard Fineman delves into the pervasive influence of television in shaping public opinion and its impact on democracy. Through a blend of , cultural analysis, and critical [...]

Advertising is everywhere in modern American culture. It shapes how we shop, what we think is normal, and even who we think we are. From billboards on city streets to sneaky product placements in TV shows, ads are always there, [...]

In recent years, the intersection of social media and mental health has emerged as a significant area of concern, particularly with regards to the alarming rates of teen suicide. Adolescence is a critical period marked by [...]

I chose to discuss minority representation in media. I wanted to write about this because I have noticed that there is a certain shortcoming of non-white actors in media. With this is mind, questions I want to address are how [...]

One of the least controversial ideas in our culture is “resource conservation”: the idea that we should prioritize conserving resources for future generations lest we leave them with nothing. This of courses implies that there [...]

Related Topics

By clicking “Send”, you agree to our Terms of service and Privacy statement . We will occasionally send you account related emails.

Where do you want us to send this sample?

By clicking “Continue”, you agree to our terms of service and privacy policy.

Be careful. This essay is not unique

This essay was donated by a student and is likely to have been used and submitted before

Download this Sample

Free samples may contain mistakes and not unique parts

Sorry, we could not paraphrase this essay. Our professional writers can rewrite it and get you a unique paper.

Please check your inbox.

We can write you a custom essay that will follow your exact instructions and meet the deadlines. Let's fix your grades together!

Get Your Personalized Essay in 3 Hours or Less!

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

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

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

Effects of Social Media Use on Psychological Well-Being: A Mediated Model

Dragana ostic, sikandar ali qalati, belem barbosa, syed mir muhammad shah, esthela galvan vela, ahmed muhammad herzallah.

• Author information
• Article notes
• Copyright and License information

Edited by: Heyla A. Selim, King Saud University, Saudi Arabia

Reviewed by: Elizabeth A. Boyle, University of the West of Scotland, United Kingdom; Barbara Caci, University of Palermo, Italy

*Correspondence: Sikandar Ali Qalati [email protected] ; [email protected]

Esthela Galvan Vela [email protected]

This article was submitted to Human-Media Interaction, a section of the journal Frontiers in Psychology

†ORCID: Dragana Ostic orcid.org/0000-0002-0469-1342

Sikandar Ali Qalati orcid.org/0000-0001-7235-6098

Belem Barbosa orcid.org/0000-0002-4057-360X

Esthela Galvan Vela orcid.org/0000-0002-8778-3989

Feng Liu orcid.org/0000-0001-9367-049X

Received 2021 Mar 10; Accepted 2021 May 25; Collection date 2021.

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.

The growth in social media use has given rise to concerns about the impacts it may have on users' psychological well-being. This paper's main objective is to shed light on the effect of social media use on psychological well-being. Building on contributions from various fields in the literature, it provides a more comprehensive study of the phenomenon by considering a set of mediators, including social capital types (i.e., bonding social capital and bridging social capital), social isolation, and smartphone addiction. The paper includes a quantitative study of 940 social media users from Mexico, using structural equation modeling (SEM) to test the proposed hypotheses. The findings point to an overall positive indirect impact of social media usage on psychological well-being, mainly due to the positive effect of bonding and bridging social capital. The empirical model's explanatory power is 45.1%. This paper provides empirical evidence and robust statistical analysis that demonstrates both positive and negative effects coexist, helping to reconcile the inconsistencies found so far in the literature.

Keywords: smartphone addiction, social isolation, bonding social capital, bridging social capital, phubbing, social media use

Introduction

The use of social media has grown substantially in recent years (Leong et al., 2019 ; Kemp, 2020 ). Social media refers to “the websites and online tools that facilitate interactions between users by providing them opportunities to share information, opinions, and interest” (Swar and Hameed, 2017 , p. 141). Individuals use social media for many reasons, including entertainment, communication, and searching for information. Notably, adolescents and young adults are spending an increasing amount of time on online networking sites, e-games, texting, and other social media (Twenge and Campbell, 2019 ). In fact, some authors (e.g., Dhir et al., 2018 ; Tateno et al., 2019 ) have suggested that social media has altered the forms of group interaction and its users' individual and collective behavior around the world.

Consequently, there are increased concerns regarding the possible negative impacts associated with social media usage addiction (Swar and Hameed, 2017 ; Kircaburun et al., 2020 ), particularly on psychological well-being (Chotpitayasunondh and Douglas, 2016 ; Jiao et al., 2017 ; Choi and Noh, 2019 ; Chatterjee, 2020 ). Smartphones sometimes distract their users from relationships and social interaction (Chotpitayasunondh and Douglas, 2016 ; Li et al., 2020a ), and several authors have stressed that the excessive use of social media may lead to smartphone addiction (Swar and Hameed, 2017 ; Leong et al., 2019 ), primarily because of the fear of missing out (Reer et al., 2019 ; Roberts and David, 2020 ). Social media usage has been associated with anxiety, loneliness, and depression (Dhir et al., 2018 ; Reer et al., 2019 ), social isolation (Van Den Eijnden et al., 2016 ; Whaite et al., 2018 ), and “phubbing,” which refers to the extent to which an individual uses, or is distracted by, their smartphone during face-to-face communication with others (Chotpitayasunondh and Douglas, 2016 ; Jiao et al., 2017 ; Choi and Noh, 2019 ; Chatterjee, 2020 ).

However, social media use also contributes to building a sense of connectedness with relevant others (Twenge and Campbell, 2019 ), which may reduce social isolation. Indeed, social media provides several ways to interact both with close ties, such as family, friends, and relatives, and weak ties, including coworkers, acquaintances, and strangers (Chen and Li, 2017 ), and plays a key role among people of all ages as they exploit their sense of belonging in different communities (Roberts and David, 2020 ). Consequently, despite the fears regarding the possible negative impacts of social media usage on well-being, there is also an increasing number of studies highlighting social media as a new communication channel (Twenge and Campbell, 2019 ; Barbosa et al., 2020 ), stressing that it can play a crucial role in developing one's presence, identity, and reputation, thus facilitating social interaction, forming and maintaining relationships, and sharing ideas (Carlson et al., 2016 ), which consequently may be significantly correlated to social support (Chen and Li, 2017 ; Holliman et al., 2021 ). Interestingly, recent studies (e.g., David et al., 2018 ; Bano et al., 2019 ; Barbosa et al., 2020 ) have suggested that the impact of smartphone usage on psychological well-being depends on the time spent on each type of application and the activities that users engage in.

Hence, the literature provides contradictory cues regarding the impacts of social media on users' well-being, highlighting both the possible negative impacts and the social enhancement it can potentially provide. In line with views on the need to further investigate social media usage (Karikari et al., 2017 ), particularly regarding its societal implications (Jiao et al., 2017 ), this paper argues that there is an urgent need to further understand the impact of the time spent on social media on users' psychological well-being, namely by considering other variables that mediate and further explain this effect.

One of the relevant perspectives worth considering is that provided by social capital theory, which is adopted in this paper. Social capital theory has previously been used to study how social media usage affects psychological well-being (e.g., Bano et al., 2019 ). However, extant literature has so far presented only partial models of associations that, although statistically acceptable and contributing to the understanding of the scope of social networks, do not provide as comprehensive a vision of the phenomenon as that proposed within this paper. Furthermore, the contradictory views, suggesting both negative (e.g., Chotpitayasunondh and Douglas, 2016 ; Van Den Eijnden et al., 2016 ; Jiao et al., 2017 ; Whaite et al., 2018 ; Choi and Noh, 2019 ; Chatterjee, 2020 ) and positive impacts (Carlson et al., 2016 ; Chen and Li, 2017 ; Twenge and Campbell, 2019 ) of social media on psychological well-being, have not been adequately explored.

Given this research gap, this paper's main objective is to shed light on the effect of social media use on psychological well-being. As explained in detail in the next section, this paper explores the mediating effect of bonding and bridging social capital. To provide a broad view of the phenomenon, it also considers several variables highlighted in the literature as affecting the relationship between social media usage and psychological well-being, namely smartphone addiction, social isolation, and phubbing. The paper utilizes a quantitative study conducted in Mexico, comprising 940 social media users, and uses structural equation modeling (SEM) to test a set of research hypotheses.

This article provides several contributions. First, it adds to existing literature regarding the effect of social media use on psychological well-being and explores the contradictory indications provided by different approaches. Second, it proposes a conceptual model that integrates complementary perspectives on the direct and indirect effects of social media use. Third, it offers empirical evidence and robust statistical analysis that demonstrates that both positive and negative effects coexist, helping resolve the inconsistencies found so far in the literature. Finally, this paper provides insights on how to help reduce the potential negative effects of social media use, as it demonstrates that, through bridging and bonding social capital, social media usage positively impacts psychological well-being. Overall, the article offers valuable insights for academics, practitioners, and society in general.

The remainder of this paper is organized as follows. Section Literature Review presents a literature review focusing on the factors that explain the impact of social media usage on psychological well-being. Based on the literature review, a set of hypotheses are defined, resulting in the proposed conceptual model, which includes both the direct and indirect effects of social media usage on psychological well-being. Section Research Methodology explains the methodological procedures of the research, followed by the presentation and discussion of the study's results in section Results. Section Discussion is dedicated to the conclusions and includes implications, limitations, and suggestions for future research.

Literature Review

Putnam ( 1995 , p. 664–665) defined social capital as “features of social life – networks, norms, and trust – that enable participants to act together more effectively to pursue shared objectives.” Li and Chen ( 2014 , p. 117) further explained that social capital encompasses “resources embedded in one's social network, which can be assessed and used for instrumental or expressive returns such as mutual support, reciprocity, and cooperation.”

Putnam ( 1995 , 2000 ) conceptualized social capital as comprising two dimensions, bridging and bonding, considering the different norms and networks in which they occur. Bridging social capital refers to the inclusive nature of social interaction and occurs when individuals from different origins establish connections through social networks. Hence, bridging social capital is typically provided by heterogeneous weak ties (Li and Chen, 2014 ). This dimension widens individual social horizons and perspectives and provides extended access to resources and information. Bonding social capital refers to the social and emotional support each individual receives from his or her social networks, particularly from close ties (e.g., family and friends).

Overall, social capital is expected to be positively associated with psychological well-being (Bano et al., 2019 ). Indeed, Williams ( 2006 ) stressed that interaction generates affective connections, resulting in positive impacts, such as emotional support. The following sub-sections use the lens of social capital theory to explore further the relationship between the use of social media and psychological well-being.

Social Media Use, Social Capital, and Psychological Well-Being

The effects of social media usage on social capital have gained increasing scholarly attention, and recent studies have highlighted a positive relationship between social media use and social capital (Brown and Michinov, 2019 ; Tefertiller et al., 2020 ). Li and Chen ( 2014 ) hypothesized that the intensity of Facebook use by Chinese international students in the United States was positively related to social capital forms. A longitudinal survey based on the quota sampling approach illustrated the positive effects of social media use on the two social capital dimensions (Chen and Li, 2017 ). Abbas and Mesch ( 2018 ) argued that, as Facebook usage increases, it will also increase users' social capital. Karikari et al. ( 2017 ) also found positive effects of social media use on social capital. Similarly, Pang ( 2018 ) studied Chinese students residing in Germany and found positive effects of social networking sites' use on social capital, which, in turn, was positively associated with psychological well-being. Bano et al. ( 2019 ) analyzed the 266 students' data and found positive effects of WhatsApp use on social capital forms and the positive effect of social capital on psychological well-being, emphasizing the role of social integration in mediating this positive effect.

Kim and Kim ( 2017 ) stressed the importance of having a heterogeneous network of contacts, which ultimately enhances the potential social capital. Overall, the manifest and social relations between people from close social circles (bonding social capital) and from distant social circles (bridging social capital) are strengthened when they promote communication, social support, and the sharing of interests, knowledge, and skills, which are shared with other members. This is linked to positive effects on interactions, such as acceptance, trust, and reciprocity, which are related to the individuals' health and psychological well-being (Bekalu et al., 2019 ), including when social media helps to maintain social capital between social circles that exist outside of virtual communities (Ellison et al., 2007 ).

Grounded on the above literature, this study proposes the following hypotheses:

H1a: Social media use is positively associated with bonding social capital.

H1b: Bonding social capital is positively associated with psychological well-being.

H2a: Social media use is positively associated with bridging social capital.

H2b: Bridging social capital is positively associated with psychological well-being.

Social Media Use, Social Isolation, and Psychological Well-Being

Social isolation is defined as “a deficit of personal relationships or being excluded from social networks” (Choi and Noh, 2019 , p. 4). The state that occurs when an individual lacks true engagement with others, a sense of social belonging, and a satisfying relationship is related to increased mortality and morbidity (Primack et al., 2017 ). Those who experience social isolation are deprived of social relationships and lack contact with others or involvement in social activities (Schinka et al., 2012 ). Social media usage has been associated with anxiety, loneliness, and depression (Dhir et al., 2018 ; Reer et al., 2019 ), and social isolation (Van Den Eijnden et al., 2016 ; Whaite et al., 2018 ). However, some recent studies have argued that social media use decreases social isolation (Primack et al., 2017 ; Meshi et al., 2020 ). Indeed, the increased use of social media platforms such as Facebook, WhatsApp, Instagram, and Twitter, among others, may provide opportunities for decreasing social isolation. For instance, the improved interpersonal connectivity achieved via videos and images on social media helps users evidence intimacy, attenuating social isolation (Whaite et al., 2018 ).

Chappell and Badger ( 1989 ) stated that social isolation leads to decreased psychological well-being, while Choi and Noh ( 2019 ) concluded that greater social isolation is linked to increased suicide risk. Schinka et al. ( 2012 ) further argued that, when individuals experience social isolation from siblings, friends, family, or society, their psychological well-being tends to decrease. Thus, based on the literature cited above, this study proposes the following hypotheses:

H3a: Social media use is significantly associated with social isolation.

H3b: Social isolation is negatively associated with psychological well-being.

Social Media Use, Smartphone Addiction, Phubbing, and Psychological Well-Being

Smartphone addiction refers to “an individuals' excessive use of a smartphone and its negative effects on his/her life as a result of his/her inability to control his behavior” (Gökçearslan et al., 2018 , p. 48). Regardless of its form, smartphone addiction results in social, medical, and psychological harm to people by limiting their ability to make their own choices (Chotpitayasunondh and Douglas, 2016 ). The rapid advancement of information and communication technologies has led to the concept of social media, e-games, and also to smartphone addiction (Chatterjee, 2020 ). The excessive use of smartphones for social media use, entertainment (watching videos, listening to music), and playing e-games is more common amongst people addicted to smartphones (Jeong et al., 2016 ). In fact, previous studies have evidenced the relationship between social use and smartphone addiction (Salehan and Negahban, 2013 ; Jeong et al., 2016 ; Swar and Hameed, 2017 ). In line with this, the following hypotheses are proposed:

H4a: Social media use is positively associated with smartphone addiction.

H4b: Smartphone addiction is negatively associated with psychological well-being.

While smartphones are bringing individuals closer, they are also, to some extent, pulling people apart (Tonacci et al., 2019 ). For instance, they can lead to individuals ignoring others with whom they have close ties or physical interactions; this situation normally occurs due to extreme smartphone use (i.e., at the dinner table, in meetings, at get-togethers and parties, and in other daily activities). This act of ignoring others is called phubbing and is considered a common phenomenon in communication activities (Guazzini et al., 2019 ; Chatterjee, 2020 ). Phubbing is also referred to as an act of snubbing others (Chatterjee, 2020 ). This term was initially used in May 2012 by an Australian advertising agency to describe the “growing phenomenon of individuals ignoring their families and friends who were called phubbee (a person who is a recipients of phubbing behavior) victim of phubber (a person who start phubbing her or his companion)” (Chotpitayasunondh and Douglas, 2018 ). Smartphone addiction has been found to be a determinant of phubbing (Kim et al., 2018 ). Other recent studies have also evidenced the association between smartphones and phubbing (Chotpitayasunondh and Douglas, 2016 ; Guazzini et al., 2019 ; Tonacci et al., 2019 ; Chatterjee, 2020 ). Vallespín et al. ( 2017 ) argued that phubbing behavior has a negative influence on psychological well-being and satisfaction. Furthermore, smartphone addiction is considered responsible for the development of new technologies. It may also negatively influence individual's psychological proximity (Chatterjee, 2020 ). Therefore, based on the above discussion and calls for the association between phubbing and psychological well-being to be further explored, this study proposes the following hypotheses:

H5: Smartphone addiction is positively associated with phubbing.

H6: Phubbing is negatively associated with psychological well-being.

Indirect Relationship Between Social Media Use and Psychological Well-Being

Beyond the direct hypotheses proposed above, this study investigates the indirect effects of social media use on psychological well-being mediated by social capital forms, social isolation, and phubbing. As described above, most prior studies have focused on the direct influence of social media use on social capital forms, social isolation, smartphone addiction, and phubbing, as well as the direct impact of social capital forms, social isolation, smartphone addiction, and phubbing on psychological well-being. Very few studies, however, have focused on and evidenced the mediating role of social capital forms, social isolation, smartphone addiction, and phubbing derived from social media use in improving psychological well-being (Chen and Li, 2017 ; Pang, 2018 ; Bano et al., 2019 ; Choi and Noh, 2019 ). Moreover, little is known about smartphone addiction's mediating role between social media use and psychological well-being. Therefore, this study aims to fill this gap in the existing literature by investigating the mediation of social capital forms, social isolation, and smartphone addiction. Further, examining the mediating influence will contribute to a more comprehensive understanding of social media use on psychological well-being via the mediating associations of smartphone addiction and psychological factors. Therefore, based on the above, we propose the following hypotheses (the conceptual model is presented in Figure 1 ):

Conceptual model.

H7: (a) Bonding social capital; (b) bridging social capital; (c) social isolation; and (d) smartphone addiction mediate the relationship between social media use and psychological well-being.

Research Methodology

Sample procedure and online survey.

This study randomly selected students from universities in Mexico. We chose University students for the following reasons. First, students are considered the most appropriate sample for e-commerce studies, particularly in the social media context (Oghazi et al., 2018 ; Shi et al., 2018 ). Second, University students are considered to be frequent users and addicted to smartphones (Mou et al., 2017 ; Stouthuysen et al., 2018 ). Third, this study ensured that respondents were experienced, well-educated, and possessed sufficient knowledge of the drawbacks of social media and the extreme use of smartphones. A total sample size of 940 University students was ultimately achieved from the 1,500 students contacted, using a convenience random sampling approach, due both to the COVID-19 pandemic and budget and time constraints. Additionally, in order to test the model, a quantitative empirical study was conducted, using an online survey method to collect data. This study used a web-based survey distributed via social media platforms for two reasons: the COVID-19 pandemic; and to reach a large number of respondents (Qalati et al., 2021 ). Furthermore, online surveys are considered a powerful and authenticated tool for new research (Fan et al., 2021 ), while also representing a fast, simple, and less costly approach to collecting data (Dutot and Bergeron, 2016 ).

Data Collection Procedures and Respondent's Information

Data were collected by disseminating a link to the survey by e-mail and social network sites. Before presenting the closed-ended questionnaire, respondents were assured that their participation would remain voluntary, confidential, and anonymous. Data collection occurred from July 2020 to December 2020 (during the pandemic). It should be noted that, because data were collected during the pandemic, this may have had an influence on the results of the study. The reason for choosing a six-month lag time was to mitigate common method bias (CMB) (Li et al., 2020b ). In the present study, 1,500 students were contacted via University e-mail and social applications (Facebook, WhatsApp, and Instagram). We sent a reminder every month for 6 months (a total of six reminders), resulting in 940 valid responses. Thus, 940 (62.6% response rate) responses were used for hypotheses testing.

Table 1 reveals that, of the 940 participants, three-quarters were female (76.4%, n = 719) and nearly one-quarter (23.6%, n = 221) were male. Nearly half of the participants (48.8%, n = 459) were aged between 26 and 35 years, followed by 36 to 35 years (21.9%, n = 206), <26 (20.3%, n = 191), and over 45 (8.9%, n = 84). Approximately two-thirds (65%, n = 611) had a bachelor's degree or above, while one-third had up to 12 years of education. Regarding the daily frequency of using the Internet, nearly half (48.6%, n = 457) of the respondents reported between 5 and 8 h a day, and over one-quarter (27.2%) 9–12 h a day. Regarding the social media platforms used, over 38.5 and 39.6% reported Facebook and WhatsApp, respectively. Of the 940 respondents, only 22.1% reported Instagram (12.8%) and Twitter (9.2%). It should be noted, however, that the sample is predominantly female and well-educated.

Respondents' characteristics.

Measurement Items

The study used five-point Likert scales (1 = “strongly disagree;” 5 = “strongly agree”) to record responses.

Social Media Use

Social media use was assessed using four items adapted from Karikari et al. ( 2017 ). Sample items include “Social media is part of my everyday activity,” “Social media has become part of my daily life,” “I would be sorry if social media shut down,” and “I feel out of touch, when I have not logged onto social media for a while.” The adapted items had robust reliability and validity (CA = 783, CR = 0.857, AVE = 0.600).

Social Capital

Social capital was measured using a total of eight items, representing bonding social capital (four items) and bridging social capital (four items) adapted from Chan ( 2015 ). Sample construct items include: bonging social capital (“I am willing to spend time to support general community activities,” “I interact with people who are quite different from me”) and bridging social capital (“My social media community is a good place to be,” “Interacting with people on social media makes me want to try new things”). The adapted items had robust reliability and validity [bonding social capital (CA = 0.785, CR = 0.861, AVE = 0.608) and bridging social capital (CA = 0.834, CR = 0.883, AVE = 0.601)].

Social Isolation

Social isolation was assessed using three items from Choi and Noh ( 2019 ). Sample items include “I do not have anyone to play with,” “I feel alone from people,” and “I have no one I can trust.” This adapted scale had substantial reliability and validity (CA = 0.890, CR = 0.928, AVE = 0.811).

Smartphone Addiction

Smartphone addiction was assessed using five items taken from Salehan and Negahban ( 2013 ). Sample items include “I am always preoccupied with my mobile,” “Using my mobile phone keeps me relaxed,” and “I am not able to control myself from frequent use of mobile phones.” Again, these adapted items showed substantial reliability and validity (CA = 903, CR = 0.928, AVE = 0.809).

Phubbing was assessed using four items from Chotpitayasunondh and Douglas ( 2018 ). Sample items include: “I have conflicts with others because I am using my phone” and “I would rather pay attention to my phone than talk to others.” This construct also demonstrated significant reliability and validity (CA = 770, CR = 0.894, AVE = 0.809).

Psychological Well-Being

Psychological well-being was assessed using five items from Jiao et al. ( 2017 ). Sample items include “I lead a purposeful and meaningful life with the help of others,” “My social relationships are supportive and rewarding in social media,” and “I am engaged and interested in my daily on social media.” This study evidenced that this adapted scale had substantial reliability and validity (CA = 0.886, CR = 0.917, AVE = 0.688).

Data Analysis

Based on the complexity of the association between the proposed construct and the widespread use and acceptance of SmartPLS 3.0 in several fields (Hair et al., 2019 ), we utilized SEM, using SmartPLS 3.0, to examine the relationships between constructs. Structural equation modeling is a multivariate statistical analysis technique that is used to investigate relationships. Further, it is a combination of factor and multivariate regression analysis, and is employed to explore the relationship between observed and latent constructs.

SmartPLS 3.0 “is a more comprehensive software program with an intuitive graphical user interface to run partial least square SEM analysis, certainly has had a massive impact” (Sarstedt and Cheah, 2019 ). According to Ringle et al. ( 2015 ), this commercial software offers a wide range of algorithmic and modeling options, improved usability, and user-friendly and professional support. Furthermore, Sarstedt and Cheah ( 2019 ) suggested that structural equation models enable the specification of complex interrelationships between observed and latent constructs. Hair et al. ( 2019 ) argued that, in recent years, the number of articles published using partial least squares SEM has increased significantly in contrast to covariance-based SEM. In addition, partial least squares SEM using SmartPLS is more appealing for several scholars as it enables them to predict more complex models with several variables, indicator constructs, and structural paths, instead of imposing distributional assumptions on the data (Hair et al., 2019 ). Therefore, this study utilized the partial least squares SEM approach using SmartPLS 3.0.

Common Method Bias (CMB) Test

This study used the Kaiser–Meyer–Olkin (KMO) test to measure the sampling adequacy and ensure data suitability. The KMO test result was 0.874, which is greater than an acceptable threshold of 0.50 (Ali Qalati et al., 2021 ; Shrestha, 2021 ), and hence considered suitable for explanatory factor analysis. Moreover, Bartlett's test results demonstrated a significance level of 0.001, which is considered good as it is below the accepted threshold of 0.05.

The term CMB is associated with Campbell and Fiske ( 1959 ), who highlighted the importance of CMB and identified that a portion of variance in the research may be due to the methods employed. It occurs when all scales of the study are measured at the same time using a single questionnaire survey (Podsakoff and Organ, 1986 ); subsequently, estimates of the relationship among the variables might be distorted by the impacts of CMB. It is considered a serious issue that has a potential to “jeopardize” the validity of the study findings (Tehseen et al., 2017 ). There are several reasons for CMB: (1) it mainly occurs due to response “tendencies that raters can apply uniformity across the measures;” and (2) it also occurs due to similarities in the wording and structure of the survey items that produce similar results (Jordan and Troth, 2019 ). Harman's single factor test and a full collinearity approach were employed to ensure that the data was free from CMB (Tehseen et al., 2017 ; Jordan and Troth, 2019 ; Ali Qalati et al., 2021 ). Harman's single factor test showed a single factor explained only 22.8% of the total variance, which is far below the 50.0% acceptable threshold (Podsakoff et al., 2003 ).

Additionally, the variance inflation factor (VIF) was used, which is a measure of the amount of multicollinearity in a set of multiple regression constructs and also considered a way of detecting CMB (Hair et al., 2019 ). Hair et al. ( 2019 ) suggested that the acceptable threshold for the VIF is 3.0; as the computed VIFs for the present study ranged from 1.189 to 1.626, CMB is not a key concern (see Table 2 ). Bagozzi et al. ( 1991 ) suggested a correlation-matrix procedure to detect CMB. Common method bias is evident if correlation among the principle constructs is >0.9 (Tehseen et al., 2020 ); however, no values >0.9 were found in this study (see section Assessment of Measurement Model). This study used a two-step approach to evaluate the measurement model and the structural model.

Common method bias (full collinearity VIF).

Assessment of Measurement Model

Before conducting the SEM analysis, the measurement model was assessed to examine individual item reliability, internal consistency, and convergent and discriminant validity. Table 3 exhibits the values of outer loading used to measure an individual item's reliability (Hair et al., 2012 ). Hair et al. ( 2017 ) proposed that the value for each outer loading should be ≥0.7; following this principle, two items of phubbing (PHUB3—I get irritated if others ask me to get off my phone and talk to them; PHUB4—I use my phone even though I know it irritated others) were removed from the analysis Hair et al. ( 2019 ). According to Nunnally ( 1978 ), Cronbach's alpha values should exceed 0.7. The threshold values of constructs in this study ranged from 0.77 to 0.903. Regarding internal consistency, Bagozzi and Yi ( 1988 ) suggested that composite reliability (CR) should be ≥0.7. The coefficient value for CR in this study was between 0.857 and 0.928. Regarding convergent validity, Fornell and Larcker ( 1981 ) suggested that the average variance extracted (AVE) should be ≥0.5. Average variance extracted values in this study were between 0.60 and 0.811. Finally, regarding discriminant validity, according to Fornell and Larcker ( 1981 ), the square root of the AVE for each construct should exceed the inter-correlations of the construct with other model constructs. That was the case in this study, as shown in Table 4 .

Study measures, factor loading, and the constructs' reliability and convergent validity.

Discriminant validity and correlation.

Bold values are the square root of the AVE .

Hence, by analyzing the results of the measurement model, it can be concluded that the data are adequate for structural equation estimation.

Assessment of the Structural Model

This study used the PLS algorithm and a bootstrapping technique with 5,000 bootstraps as proposed by Hair et al. ( 2019 ) to generate the path coefficient values and their level of significance. The coefficient of determination ( R 2 ) is an important measure to assess the structural model and its explanatory power (Henseler et al., 2009 ; Hair et al., 2019 ). Table 5 and Figure 2 reveal that the R 2 value in the present study was 0.451 for psychological well-being, which means that 45.1% of changes in psychological well-being occurred due to social media use, social capital forms (i.e., bonding and bridging), social isolation, smartphone addiction, and phubbing. Cohen ( 1998 ) proposed that R 2 values of 0.60, 0.33, and 0.19 are considered substantial, moderate, and weak. Following Cohen's ( 1998 ) threshold values, this research demonstrates a moderate predicting power for psychological well-being among Mexican respondents ( Table 6 ).

Summary of path coefficients and hypothesis testing.

p-value < 0.05, t-value > 1.96 .

Structural model.

Strength of the model (Predictive relevance, coefficient of determination, and model fit indices).

Goodness of fit → SRMR = 0.063; d_ULS = 1.589; d_G = 0.512; chi-square = 2,910.744 .

Apart from the R 2 measure, the present study also used cross-validated redundancy measures, or effect sizes ( q 2 ), to assess the proposed model and validate the results (Ringle et al., 2012 ). Hair et al. ( 2019 ) suggested that a model exhibiting an effect size q 2 > 0 has predictive relevance ( Table 6 ). This study's results evidenced that it has a 0.15 <0.29 <0.35 (medium) predictive relevance, as 0.02, 0.15, and 0.35 are considered small, medium, and large, respectively (Cohen, 1998 ). Regarding the goodness-of-fit indices, Hair et al. ( 2019 ) suggested the standardized root mean square residual (SRMR) to evaluate the goodness of fit. Standardized root mean square is an absolute measure of fit: a value of zero indicates perfect fit and a value <0.08 is considered good fit (Hair et al., 2019 ). This study exhibits an adequate model fitness level with an SRMR value of 0.063 ( Table 6 ).

Table 5 reveals that all hypotheses of the study were accepted base on the criterion ( p -value < 0.05). H1a (β = 0.332, t = 10.283, p = 0.001) was confirmed, with the second most robust positive and significant relationship (between social media use and bonding social capital). In addition, this study evidenced a positive and significant relationship between bonding social capital and psychological well-being (β = 0.127, t = 4.077, p = 0.001); therefore, H1b was accepted. Regarding social media use and bridging social capital, the present study found the most robust positive and significant impact (β = 0.439, t = 15.543, p = 0.001); therefore, H2a was accepted. The study also evidenced a positive and significant association between bridging social capital and psychological well-being (β = 0.561, t = 20.953, p = 0.001); thus, H2b was accepted. The present study evidenced a significant effect of social media use on social isolation (β = 0.145, t = 4.985, p = 0.001); thus, H3a was accepted. In addition, this study accepted H3b (β = −0.051, t = 2.01, p = 0.044). Furthermore, this study evidenced a positive and significant effect of social media use on smartphone addiction (β = 0.223, t = 6.241, p = 0.001); therefore, H4a was accepted. Furthermore, the present study found that smartphone addiction has a negative significant influence on psychological well-being (β = −0.068, t = 2.387, p = 0.017); therefore, H4b was accepted. Regarding the relationship between smartphone addiction and phubbing, this study found a positive and significant effect of smartphone addiction on phubbing (β = 0.244, t = 7.555, p = 0.001); therefore, H5 was accepted. Furthermore, the present research evidenced a positive and significant influence of phubbing on psychological well-being (β = 0.137, t = 4.938, p = 0.001); therefore, H6 was accepted. Finally, the study provides interesting findings on the indirect effect of social media use on psychological well-being ( t -value > 1.96 and p -value < 0.05); therefore, H7a–d were accepted.

Furthermore, to test the mediating analysis, Preacher and Hayes's ( 2008 ) approach was used. The key characteristic of an indirect relationship is that it involves a third construct, which plays a mediating role in the relationship between the independent and dependent constructs. Logically, the effect of A (independent construct) on C (the dependent construct) is mediated by B (a third variable). Preacher and Hayes ( 2008 ) suggested the following: B is a construct acting as a mediator if A significantly influences B, A significantly accounts for variability in C, B significantly influences C when controlling for A, and the influence of A on C decreases significantly when B is added simultaneously with A as a predictor of C. According to Matthews et al. ( 2018 ), if the indirect effect is significant while the direct insignificant, full mediation has occurred, while if both direct and indirect effects are substantial, partial mediation has occurred. This study evidenced that there is partial mediation in the proposed construct ( Table 5 ). Following Preacher and Hayes ( 2008 ) this study evidenced that there is partial mediation in the proposed construct, because the relationship between independent variable (social media use) and dependent variable (psychological well-being) is significant ( p -value < 0.05) and indirect effect among them after introducing mediator (bonding social capital, bridging social capital, social isolation, and smartphone addiction) is also significant ( p -value < 0.05), therefore it is evidenced that when there is a significant effect both direct and indirect it's called partial mediation.

The present study reveals that the social and psychological impacts of social media use among University students is becoming more complex as there is continuing advancement in technology, offering a range of affordable interaction opportunities. Based on the 940 valid responses collected, all the hypotheses were accepted ( p < 0.05).

H1a finding suggests that social media use is a significant influencing factor of bonding social capital. This implies that, during a pandemic, social media use enables students to continue their close relationships with family members, friends, and those with whom they have close ties. This finding is in line with prior work of Chan ( 2015 ) and Ellison et al. ( 2007 ), who evidenced that social bonding capital is predicted by Facebook use and having a mobile phone. H1b findings suggest that, when individuals believe that social communication can help overcome obstacles to interaction and encourage more virtual self-disclosure, social media use can improve trust and promote the establishment of social associations, thereby enhancing well-being. These findings are in line with those of Gong et al. ( 2021 ), who also witnessed the significant effect of bonding social capital on immigrants' psychological well-being, subsequently calling for the further evidence to confirm the proposed relationship.

The findings of the present study related to H2a suggest that students are more likely to use social media platforms to receive more emotional support, increase their ability to mobilize others, and to build social networks, which leads to social belongingness. Furthermore, the findings suggest that social media platforms enable students to accumulate and maintain bridging social capital; further, online classes can benefit students who feel shy when participating in offline classes. This study supports the previous findings of Chan ( 2015 ) and Karikari et al. ( 2017 ). Notably, the present study is not limited to a single social networking platform, taking instead a holistic view of social media. The H2b findings are consistent with those of Bano et al. ( 2019 ), who also confirmed the link between bonding social capital and psychological well-being among University students using WhatsApp as social media platform, as well as those of Chen and Li ( 2017 ).

The H3a findings suggest that, during the COVID-19 pandemic when most people around the world have had limited offline or face-to-face interaction and have used social media to connect with families, friends, and social communities, they have often been unable to connect with them. This is due to many individuals avoiding using social media because of fake news, financial constraints, and a lack of trust in social media; thus, the lack both of offline and online interaction, coupled with negative experiences on social media use, enhances the level of social isolation (Hajek and König, 2021 ). These findings are consistent with those of Adnan and Anwar ( 2020 ). The H3b suggests that higher levels of social isolation have a negative impact on psychological well-being. These result indicating that, consistent with Choi and Noh ( 2019 ), social isolation is negatively and significantly related to psychological well-being.

The H4a results suggests that substantial use of social media use leads to an increase in smartphone addiction. These findings are in line with those of Jeong et al. ( 2016 ), who stated that the excessive use of smartphones for social media, entertainment (watching videos, listening to music), and playing e-games was more likely to lead to smartphone addiction. These findings also confirm the previous work of Jeong et al. ( 2016 ), Salehan and Negahban ( 2013 ), and Swar and Hameed ( 2017 ). The H4b results revealed that a single unit increase in smartphone addiction results in a 6.8% decrease in psychological well-being. These findings are in line with those of Tangmunkongvorakul et al. ( 2019 ), who showed that students with higher levels of smartphone addiction had lower psychological well-being scores. These findings also support those of Shoukat ( 2019 ), who showed that smartphone addiction inversely influences individuals' mental health.

This suggests that the greater the smartphone addiction, the greater the phubbing. The H5 findings are in line with those of Chatterjee ( 2020 ), Chotpitayasunondh and Douglas ( 2016 ), Guazzini et al. ( 2019 ), and Tonacci et al. ( 2019 ), who also evidenced a significant impact of smartphone addiction and phubbing. Similarly, Chotpitayasunondh and Douglas ( 2018 ) corroborated that smartphone addiction is the main predictor of phubbing behavior. However, these findings are inconsistent with those of Vallespín et al. ( 2017 ), who found a negative influence of phubbing.

The H6 results suggests that phubbing is one of the significant predictors of psychological well-being. Furthermore, these findings suggest that, when phubbers use a cellphone during interaction with someone, especially during the current pandemic, and they are connected with many family members, friends, and relatives; therefore, this kind of action gives them more satisfaction, which simultaneously results in increased relaxation and decreased depression (Chotpitayasunondh and Douglas, 2018 ). These findings support those of Davey et al. ( 2018 ), who evidenced that phubbing has a significant influence on adolescents and social health students in India.

The findings showed a significant and positive effect of social media use on psychological well-being both through bridging and bonding social capital. However, a significant and negative effect of social media use on psychological well-being through smartphone addiction and through social isolation was also found. Hence, this study provides evidence that could shed light on the contradictory contributions in the literature suggesting both positive (e.g., Chen and Li, 2017 ; Twenge and Campbell, 2019 ; Roberts and David, 2020 ) and negative (e.g., Chotpitayasunondh and Douglas, 2016 ; Jiao et al., 2017 ; Choi and Noh, 2019 ; Chatterjee, 2020 ) effects of social media use on psychological well-being. This study concludes that the overall impact is positive, despite some degree of negative indirect impact.

Theoretical Contributions

This study's findings contribute to the current literature, both by providing empirical evidence for the relationships suggested by extant literature and by demonstrating the relevance of adopting a more complex approach that considers, in particular, the indirect effect of social media on psychological well-being. As such, this study constitutes a basis for future research (Van Den Eijnden et al., 2016 ; Whaite et al., 2018 ) aiming to understand the impacts of social media use and to find ways to reduce its possible negative impacts.

In line with Kim and Kim ( 2017 ), who stressed the importance of heterogeneous social networks in improving social capital, this paper suggests that, to positively impact psychological well-being, social media usage should be associated both with strong and weak ties, as both are important in building social capital, and hence associated with its bonding and bridging facets. Interestingly, though, bridging capital was shown as having the greatest impact on psychological well-being. Thus, the importance of wider social horizons, the inclusion in different groups, and establishing new connections (Putnam, 1995 , 2000 ) with heterogeneous weak ties (Li and Chen, 2014 ) are highlighted in this paper.

Practical Contributions

These findings are significant for practitioners, particularly those interested in dealing with the possible negative impacts of social media use on psychological well-being. Although social media use is associated with factors that negatively impact psychological well-being, particularly smartphone addiction and social isolation, these negative impacts can be lessened if the connections with both strong and weak ties are facilitated and featured by social media. Indeed, social media platforms offer several features, from facilitating communication with family, friends, and acquaintances, to identifying and offering access to other people with shared interests. However, it is important to access heterogeneous weak ties (Li and Chen, 2014 ) so that social media offers access to wider sources of information and new resources, hence enhancing bridging social capital.

Limitations and Directions for Future Studies

This study is not without limitations. For example, this study used a convenience sampling approach to reach to a large number of respondents. Further, this study was conducted in Mexico only, limiting the generalizability of the results; future research should therefore use a cross-cultural approach to investigate the impacts of social media use on psychological well-being and the mediating role of proposed constructs (e.g., bonding and bridging social capital, social isolation, and smartphone addiction). The sample distribution may also be regarded as a limitation of the study because respondents were mainly well-educated and female. Moreover, although Internet channels represent a particularly suitable way to approach social media users, the fact that this study adopted an online survey does not guarantee a representative sample of the population. Hence, extrapolating the results requires caution, and study replication is recommended, particularly with social media users from other countries and cultures. The present study was conducted in the context of mainly University students, primarily well-educated females, via an online survey on in Mexico; therefore, the findings represent a snapshot at a particular time. Notably, however, the effect of social media use is increasing due to COVID-19 around the globe and is volatile over time.

Two of the proposed hypotheses of this study, namely the expected negative impacts of social media use on social isolation and of phubbing on psychological well-being, should be further explored. One possible approach is to consider the type of connections (i.e., weak and strong ties) to explain further the impact of social media usage on social isolation. Apparently, the prevalence of weak ties, although facilitating bridging social capital, may have an adverse impact in terms of social isolation. Regarding phubbing, the fact that the findings point to a possible positive impact on psychological well-being should be carefully addressed, specifically by psychology theorists and scholars, in order to identify factors that may help further understand this phenomenon. Other suggestions for future research include using mixed-method approaches, as qualitative studies could help further validate the results and provide complementary perspectives on the relationships between the considered variables.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics Statement

The studies involving human participants were reviewed and approved by Jiangsu University. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

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.

Funding. This study is supported by the National Statistics Research Project of China (2016LY96).

• Abbas R., Mesch G. (2018). Do rich teens get richer? Facebook use and the link between offline and online social capital among Palestinian youth in Israel. Inf. Commun. Soc. 21, 63–79. 10.1080/1369118X.2016.1261168 [ DOI ] [ Google Scholar ]
• Adnan M., Anwar K. (2020). Online learning amid the COVID-19 pandemic: students' perspectives. J. Pedagog. Sociol. Psychol. 2, 45–51. 10.33902/JPSP.202026130933083098 [ DOI ] [ Google Scholar ]
• Ali Qalati S., Li W., Ahmed N., Ali Mirani M., Khan A. (2021). Examining the factors affecting SME performance: the mediating role of social media adoption. Sustainability 13:75. 10.3390/su13010075 [ DOI ] [ Google Scholar ]
• Bagozzi R. P., Yi Y. (1988). On the evaluation of structural equation models. J. Acad. Mark. Sci. 16, 74–94. 10.1007/BF02723327 [ DOI ] [ Google Scholar ]
• Bagozzi R. P., Yi Y., Phillips L. W. (1991). Assessing construct validity in organizational research. Admin. Sci. Q. 36, 421–458. 10.2307/2393203 [ DOI ] [ Google Scholar ]
• Bano S., Cisheng W., Khan A. N., Khan N. A. (2019). WhatsApp use and student's psychological well-being: role of social capital and social integration. Child. Youth Serv. Rev. 103, 200–208. 10.1016/j.childyouth.2019.06.002 [ DOI ] [ Google Scholar ]
• Barbosa B., Chkoniya V., Simoes D., Filipe S., Santos C. A. (2020). Always connected: generation Y smartphone use and social capital. Rev. Ibérica Sist. Tecnol. Inf. E 35, 152–166. [ Google Scholar ]
• Bekalu M. A., McCloud R. F., Viswanath K. (2019). Association of social media use with social well-being, positive mental health, and self-rated health: disentangling routine use from emotional connection to use. Health Educ. Behav. 46(2 Suppl), 69S−80S. 10.1177/1090198119863768 [ DOI ] [ PubMed ] [ Google Scholar ]
• Brown G., Michinov N. (2019). Measuring latent ties on Facebook: a novel approach to studying their prevalence and relationship with bridging social capital. Technol. Soc. 59:101176. 10.1016/j.techsoc.2019.101176 [ DOI ] [ Google Scholar ]
• Campbell D. T., Fiske D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychol. Bull. 56, 81–105. 10.1037/h0046016 [ DOI ] [ PubMed ] [ Google Scholar ]
• Carlson J. R., Zivnuska S., Harris R. B., Harris K. J., Carlson D. S. (2016). Social media use in the workplace: a study of dual effects. J. Org. End User Comput. 28, 15–31. 10.4018/JOEUC.2016010102 [ DOI ] [ Google Scholar ]
• Chan M. (2015). Mobile phones and the good life: examining the relationships among mobile use, social capital and subjective well-being. New Media Soc. 17, 96–113. 10.1177/1461444813516836 [ DOI ] [ Google Scholar ]
• Chappell N. L., Badger M. (1989). Social isolation and well-being. J. Gerontol. 44, S169–S176. 10.1093/geronj/44.5.s169 [ DOI ] [ PubMed ] [ Google Scholar ]
• Chatterjee S. (2020). Antecedents of phubbing: from technological and psychological perspectives. J. Syst. Inf. Technol. 22, 161–118. 10.1108/JSIT-05-2019-0089 [ DOI ] [ Google Scholar ]
• Chen H.-T., Li X. (2017). The contribution of mobile social media to social capital and psychological well-being: examining the role of communicative use, friending and self-disclosure. Comput. Hum. Behav. 75, 958–965. 10.1016/j.chb.2017.06.011 [ DOI ] [ Google Scholar ]
• Choi D.-H., Noh G.-Y. (2019). The influence of social media use on attitude toward suicide through psychological well-being, social isolation, and social support. Inf. Commun. Soc. 23, 1–17. 10.1080/1369118X.2019.1574860 [ DOI ] [ Google Scholar ]
• Chotpitayasunondh V., Douglas K. M. (2016). How phubbing becomes the norm: the antecedents and consequences of snubbing via smartphone. Comput. Hum. Behav. 63, 9–18. 10.1016/j.chb.2016.05.018 [ DOI ] [ Google Scholar ]
• Chotpitayasunondh V., Douglas K. M. (2018). The effects of phubbing on social interaction. J. Appl. Soc. Psychol. 48, 304–316. 10.1111/jasp.12506 [ DOI ] [ Google Scholar ]
• Cohen J. (1998). Statistical Power Analysis for the Behavioural Sciences. Hillsdale, NJ: Lawrence Erlbaum Associates. [ Google Scholar ]
• Davey S., Davey A., Raghav S. K., Singh J. V., Singh N., Blachnio A., et al. (2018). Predictors and consequences of phubbing among adolescents and youth in India: an impact evaluation study. J. Fam. Community Med. 25, 35–42. 10.4103/jfcm.JFCM_71_17 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• David M. E., Roberts J. A., Christenson B. (2018). Too much of a good thing: investigating the association between actual smartphone use and individual well-being. Int. J. Hum. Comput. Interact. 34, 265–275. 10.1080/10447318.2017.1349250 [ DOI ] [ Google Scholar ]
• Dhir A., Yossatorn Y., Kaur P., Chen S. (2018). Online social media fatigue and psychological wellbeing—a study of compulsive use, fear of missing out, fatigue, anxiety and depression. Int. J. Inf. Manag. 40, 141–152. 10.1016/j.ijinfomgt.2018.01.012 [ DOI ] [ Google Scholar ]
• Dutot V., Bergeron F. (2016). From strategic orientation to social media orientation: improving SMEs' performance on social media. J. Small Bus. Enterp. Dev. 23, 1165–1190. 10.1108/JSBED-11-2015-0160 [ DOI ] [ Google Scholar ]
• Ellison N. B., Steinfield C., Lampe C. (2007). The benefits of Facebook friends: Social capital and college students' use of online social network sites. J. Comput. Mediat. Commun. 12, 1143–1168. 10.1111/j.1083-6101.2007.00367.x [ DOI ] [ Google Scholar ]
• Fan M., Huang Y., Qalati S. A., Shah S. M. M., Ostic D., Pu Z. (2021). Effects of information overload, communication overload, and inequality on digital distrust: a cyber-violence behavior mechanism. Front. Psychol. 12:643981. 10.3389/fpsyg.2021.643981 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Fornell C., Larcker D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. J. Market. Res. 18, 39–50. 10.1177/002224378101800104 [ DOI ] [ Google Scholar ]
• Gökçearslan S., Uluyol Ç., Sahin S. (2018). Smartphone addiction, cyberloafing, stress and social support among University students: a path analysis. Child. Youth Serv. Rev. 91, 47–54. 10.1016/j.childyouth.2018.05.036 [ DOI ] [ Google Scholar ]
• Gong S., Xu P., Wang S. (2021). Social capital and psychological well-being of Chinese immigrants in Japan. Int. J. Environ. Res. Public Health 18:547. 10.3390/ijerph18020547 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Guazzini A., Duradoni M., Capelli A., Meringolo P. (2019). An explorative model to assess individuals' phubbing risk. Fut. Internet 11:21. 10.3390/fi11010021 [ DOI ] [ Google Scholar ]
• Hair J. F., Risher J. J., Sarstedt M., Ringle C. M. (2019). When to use and how to report the results of PLS-SEM. Eur. Bus. Rev. 31, 2–24. 10.1108/EBR-11-2018-0203 [ DOI ] [ Google Scholar ]
• Hair J. F., Sarstedt M., Pieper T. M., Ringle C. M. (2012). The use of partial least squares structural equation modeling in strategic management research: a review of past practices and recommendations for future applications. Long Range Plann. 45, 320–340. 10.1016/j.lrp.2012.09.008 [ DOI ] [ Google Scholar ]
• Hair J. F., Sarstedt M., Ringle C. M., Gudergan S. P. (2017). Advanced Issues in Partial Least Squares Structural Equation Modeling. Thousand Oaks, CA: Sage. [ Google Scholar ]
• Hajek A., König H.-H. (2021). Social isolation and loneliness of older adults in times of the CoViD-19 pandemic: can use of online social media sites and video chats assist in mitigating social isolation and loneliness? Gerontology 67, 121–123. 10.1159/000512793 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Henseler J., Ringle C. M., Sinkovics R. R. (2009). The use of partial least squares path modeling in international marketing, in New Challenges to International Marketing, Vol. 20, eds R.R. Sinkovics and P.N. Ghauri (Bigley: Emerald; ), 277–319. [ Google Scholar ]
• Holliman A. J., Waldeck D., Jay B., Murphy S., Atkinson E., Collie R. J., et al. (2021). Adaptability and social support: examining links with psychological wellbeing among UK students and non-students. Fron. Psychol. 12:636520. 10.3389/fpsyg.2021.636520 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Jeong S.-H., Kim H., Yum J.-Y., Hwang Y. (2016). What type of content are smartphone users addicted to? SNS vs. games. Comput. Hum. Behav. 54, 10–17. 10.1016/j.chb.2015.07.035 [ DOI ] [ Google Scholar ]
• Jiao Y., Jo M.-S., Sarigöllü E. (2017). Social value and content value in social media: two paths to psychological well-being. J. Org. Comput. Electr. Commer. 27, 3–24. 10.1080/10919392.2016.1264762 [ DOI ] [ Google Scholar ]
• Jordan P. J., Troth A. C. (2019). Common method bias in applied settings: the dilemma of researching in organizations. Austr. J. Manag. 45, 3–14. 10.1177/0312896219871976 [ DOI ] [ Google Scholar ]
• Karikari S., Osei-Frimpong K., Owusu-Frimpong N. (2017). Evaluating individual level antecedents and consequences of social media use in Ghana. Technol. Forecast. Soc. Change 123, 68–79. 10.1016/j.techfore.2017.06.023 [ DOI ] [ Google Scholar ]
• Kemp S. (January 30, 2020). Digital 2020: 3.8 billion people use social media. We Are Social . Available online at: https://wearesocial.com/blog/2020/01/digital-2020-3-8-billion-people-use-social-media .
• Kim B., Kim Y. (2017). College students' social media use and communication network heterogeneity: implications for social capital and subjective well-being. Comput. Hum. Behav. 73, 620–628. 10.1016/j.chb.2017.03.033 [ DOI ] [ Google Scholar ]
• Kim K., Milne G. R., Bahl S. (2018). Smart phone addiction and mindfulness: an intergenerational comparison. Int. J. Pharmaceut. Healthcare Market. 12, 25–43. 10.1108/IJPHM-08-2016-0044 [ DOI ] [ Google Scholar ]
• Kircaburun K., Alhabash S., Tosuntaş S. B., Griffiths M. D. (2020). Uses and gratifications of problematic social media use among University students: a simultaneous examination of the big five of personality traits, social media platforms, and social media use motives. Int. J. Mental Health Addict. 18, 525–547. 10.1007/s11469-018-9940-6 [ DOI ] [ Google Scholar ]
• Leong L.-Y., Hew T.-S., Ooi K.-B., Lee V.-H., Hew J.-J. (2019). A hybrid SEM-neural network analysis of social media addiction. Expert Syst. Appl. 133, 296–316. 10.1016/j.eswa.2019.05.024 [ DOI ] [ Google Scholar ]
• Li L., Griffiths M. D., Mei S., Niu Z. (2020a). Fear of missing out and smartphone addiction mediates the relationship between positive and negative affect and sleep quality among Chinese University students. Front. Psychiatr. 11:877. 10.3389/fpsyt.2020.00877 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Li W., Qalati S. A., Khan M. A. S., Kwabena G. Y., Erusalkina D., Anwar F. (2020b). Value co-creation and growth of social enterprises in developing countries: moderating role of environmental dynamics. Entrep. Res. J. 2020:20190359. 10.1515/erj-2019-0359 [ DOI ] [ Google Scholar ]
• Li X., Chen W. (2014). Facebook or Renren? A comparative study of social networking site use and social capital among Chinese international students in the United States. Comput. Hum. Behav. 35, 116–123. 10.1016/j.chb.2014.02.012 [ DOI ] [ Google Scholar ]
• Matthews L., Hair J. F., Matthews R. (2018). PLS-SEM: the holy grail for advanced analysis. Mark. Manag. J. 28, 1–13. [ Google Scholar ]
• Meshi D., Cotten S. R., Bender A. R. (2020). Problematic social media use and perceived social isolation in older adults: a cross-sectional study. Gerontology 66, 160–168. 10.1159/000502577 [ DOI ] [ PubMed ] [ Google Scholar ]
• Mou J., Shin D.-H., Cohen J. (2017). Understanding trust and perceived usefulness in the consumer acceptance of an e-service: a longitudinal investigation. Behav. Inf. Technol. 36, 125–139. 10.1080/0144929X.2016.1203024 [ DOI ] [ Google Scholar ]
• Nunnally J. (1978). Psychometric Methods. New York, NY: McGraw-Hill. [ Google Scholar ]
• Oghazi P., Karlsson S., Hellström D., Hjort K. (2018). Online purchase return policy leniency and purchase decision: mediating role of consumer trust. J. Retail. Consumer Serv. 41, 190–200. [ Google Scholar ]
• Pang H. (2018). Exploring the beneficial effects of social networking site use on Chinese students' perceptions of social capital and psychological well-being in Germany. Int. J. Intercult. Relat. 67, 1–11. 10.1016/j.ijintrel.2018.08.002 [ DOI ] [ Google Scholar ]
• Podsakoff P. M., MacKenzie S. B., Lee J.-Y., Podsakoff N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. J. Appl. Psychol. 88, 879–903. 10.1037/0021-9010.88.5.879 [ DOI ] [ PubMed ] [ Google Scholar ]
• Podsakoff P. M., Organ D. W. (1986). Self-reports in organizational research: problems and prospects. J. Manag. 12, 531–544. 10.1177/0149206386012004088452065 [ DOI ] [ Google Scholar ]
• Preacher K. J., Hayes A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behav Res. Methods 40, 879–891. 10.3758/brm.40.3.879 [ DOI ] [ PubMed ] [ Google Scholar ]
• Primack B. A., Shensa A., Sidani J. E., Whaite E. O., yi Lin L., Rosen D., et al. (2017). Social media use and perceived social isolation among young adults in the US. Am. J. Prev. Med. 53, 1–8. 10.1016/j.amepre.2017.01.010 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Putnam R. D. (1995). Tuning in, tuning out: the strange disappearance of social capital in America. Polit. Sci. Polit. 28, 664–684. 10.2307/420517 [ DOI ] [ Google Scholar ]
• Putnam R. D. (2000). Bowling Alone: The Collapse and Revival of American Community. New York, NY: Simon and Schuster. [ Google Scholar ]
• Qalati S. A., Ostic D., Fan M., Dakhan S. A., Vela E. G., Zufar Z., et al. (2021). The general public knowledge, attitude, and practices regarding COVID-19 during the lockdown in Asian developing countries. Int. Q. Commun. Health Educ. 2021:272684X211004945. 10.1177/0272684X211004945 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Reer F., Tang W. Y., Quandt T. (2019). Psychosocial well-being and social media engagement: the mediating roles of social comparison orientation and fear of missing out. New Media Soc. 21, 1486–1505. 10.1177/1461444818823719 [ DOI ] [ Google Scholar ]
• Ringle C., Wende S., Becker J. (2015). SmartPLS 3 [software]. Bönningstedt: SmartPLS. [ Google Scholar ]
• Ringle C. M., Sarstedt M., Straub D. (2012). A critical look at the use of PLS-SEM in MIS Quarterly. MIS Q . 36, iii–xiv. 10.2307/41410402 [ DOI ] [ Google Scholar ]
• Roberts J. A., David M. E. (2020). The social media party: fear of missing out (FoMO), social media intensity, connection, and well-being. Int. J. Hum. Comput. Interact. 36, 386–392. 10.1080/10447318.2019.1646517 [ DOI ] [ Google Scholar ]
• Salehan M., Negahban A. (2013). Social networking on smartphones: when mobile phones become addictive. Comput. Hum. Behav. 29, 2632–2639. 10.1016/j.chb.2013.07.003 [ DOI ] [ Google Scholar ]
• Sarstedt M., Cheah J.-H. (2019). Partial least squares structural equation modeling using SmartPLS: a software review. J. Mark. Anal. 7, 196–202. 10.1057/s41270-019-00058-3 [ DOI ] [ Google Scholar ]
• Schinka K. C., VanDulmen M. H., Bossarte R., Swahn M. (2012). Association between loneliness and suicidality during middle childhood and adolescence: longitudinal effects and the role of demographic characteristics. J. Psychol. Interdiscipl. Appl. 146, 105–118. 10.1080/00223980.2011.584084 [ DOI ] [ PubMed ] [ Google Scholar ]
• Shi S., Mu R., Lin L., Chen Y., Kou G., Chen X.-J. (2018). The impact of perceived online service quality on swift guanxi. Internet Res. 28, 432–455. 10.1108/IntR-12-2016-0389 [ DOI ] [ Google Scholar ]
• Shoukat S. (2019). Cell phone addiction and psychological and physiological health in adolescents. EXCLI J. 18, 47–50. 10.17179/excli2018-2006 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Shrestha N. (2021). Factor analysis as a tool for survey analysis. Am. J. Appl. Math. Stat. 9, 4–11. 10.12691/ajams-9-1-215330692 [ DOI ] [ Google Scholar ]
• Stouthuysen K., Teunis I., Reusen E., Slabbinck H. (2018). Initial trust and intentions to buy: The effect of vendor-specific guarantees, customer reviews and the role of online shopping experience. Electr. Commer. Res. Appl. 27, 23–38. 10.1016/j.elerap.2017.11.002 [ DOI ] [ Google Scholar ]
• Swar B., Hameed T. (2017). Fear of missing out, social media engagement, smartphone addiction and distraction: moderating role of self-help mobile apps-based interventions in the youth , Paper presented at the 10th International Conference on Health Informatics (Porto). [ Google Scholar ]
• Tangmunkongvorakul A., Musumari P. M., Thongpibul K., Srithanaviboonchai K., Techasrivichien T., Suguimoto S. P., et al. (2019). Association of excessive smartphone use with psychological well-being among University students in Chiang Mai, Thailand. PLoS ONE 14:e0210294. 10.1371/journal.pone.0210294 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Tateno M., Teo A. R., Ukai W., Kanazawa J., Katsuki R., Kubo H., et al. (2019). Internet addiction, smartphone addiction, and hikikomori trait in Japanese young adult: social isolation and social network. Front. Psychiatry 10:455. 10.3389/fpsyt.2019.00455 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Tefertiller A. C., Maxwell L. C., Morris D. L. (2020). Social media goes to the movies: fear of missing out, social capital, and social motivations of cinema attendance. Mass Commun. Soc. 23, 378–399. 10.1080/15205436.2019.1653468 [ DOI ] [ Google Scholar ]
• Tehseen S., Qureshi Z. H., Johara F., Ramayah T. (2020). Assessing dimensions of entrepreneurial competencies: a type II (reflective-formative) measurement approach using PLS-SEM. J. Sustain. Sci. Manage. 15, 108–145. [ Google Scholar ]
• Tehseen S., Ramayah T., Sajilan S. (2017). Testing and controlling for common method variance: a review of available methods. J. Manag. Sci. 4, 146–165. 10.20547/jms.2014.1704202 [ DOI ] [ Google Scholar ]
• Tonacci A., Billeci L., Sansone F., Masci A., Pala A. P., Domenici C., et al. (2019). An innovative, unobtrusive approach to investigate smartphone interaction in nonaddicted subjects based on wearable sensors: a pilot study. Medicina (Kaunas) 55:37. 10.3390/medicina55020037 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Twenge J. M., Campbell W. K. (2019). Media use is linked to lower psychological well-being: evidence from three datasets. Psychiatr. Q. 90, 311–331. 10.1007/s11126-019-09630-7 [ DOI ] [ PubMed ] [ Google Scholar ]
• Vallespín M., Molinillo S., Muñoz-Leiva F. (2017). Segmentation and explanation of smartphone use for travel planning based on socio-demographic and behavioral variables. Ind. Manag. Data Syst. 117, 605–619. 10.1108/IMDS-03-2016-0089 [ DOI ] [ Google Scholar ]
• Van Den Eijnden R. J., Lemmens J. S., Valkenburg P. M. (2016). The social media disorder scale. Comput. Hum. Behav. 61, 478–487. 10.1016/j.chb.2016.03.038 [ DOI ] [ Google Scholar ]
• Whaite E. O., Shensa A., Sidani J. E., Colditz J. B., Primack B. A. (2018). Social media use, personality characteristics, and social isolation among young adults in the United States. Pers. Indiv. Differ. 124, 45–50. 10.1016/j.paid.2017.10.030 [ DOI ] [ Google Scholar ]
• Williams D. (2006). On and off the'net: scales for social capital in an online era. J. Comput. Mediat. Commun. 11, 593–628. 10.1016/j.1083-6101.2006.00029.x [ DOI ] [ Google Scholar ]

Associated Data

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

• View on publisher site
• PDF (723.2 KB)
• Collections

Similar articles

Cited by other articles, links to ncbi databases.

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

Add to Collections