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Thesis Statements about Social Media: 21 Examples and Tips

• by Judy Jeni
• January 27, 2024

A thesis statement is a sentence in the introduction paragraph of an essay that captures the purpose of the essay. Using thesis statements about social media as an example, I will guide you on how to write them well.

It can appear anywhere in the first paragraph of the essay but it is mostly preferred when it ends the introduction paragraph. learning how to write a thesis statement for your essay will keep you focused.

A thesis statement can be more than one sentence only when the essay is on complex topics and there is a need to break the statement into two. This means, a good thesis statement structures an essay and tells the reader what an essay is all about.

A good social media thesis statement should be about a specific aspect of social media and not just a broad view of the topic.

The statement should be on the last sentence of the first paragraph and should tell the reader about your stand on the social media issue you are presenting or arguing in the essay.

Reading an essay without a thesis statement is like solving a puzzle. Readers will have to read the conclusion to at least grasp what the essay is all about. It is therefore advisable to craft a thesis immediately after researching an essay.

Throughout your entire writing, every point in every paragraph should connect to the thesis.  In case it doesn’t then probably you have diverged from the main issue of the essay.

How to Write a Thesis Statement?

Writing a thesis statement is important when writing an essay on any topic, not just about social media. It is the key to holding your ideas and arguments together into just one sentence.

The following are tips on how to write a good thesis statement:

Start With a Question and Develop an Answer

If the question is not provided, come up with your own. Start by deciding the topic and what you would like to find out about it.

Secondly, after doing some initial research on the topic find the answers to the topic that will help and guide the process of researching and writing.

Consequently, if you write a thesis statement that does not provide information about your research topic, you need to construct it again.

Be Specific

The main idea of your essay should be specific. Therefore, the thesis statement of your essay should not be vague. When your thesis statement is too general, the essay will try to incorporate a lot of ideas that can contribute to the loss of focus on the main ideas.

Similarly, specific and narrow thesis statements help concentrate your focus on evidence that supports your essay. In like manner, a specific thesis statement tells the reader directly what to expect in the essay.

Make the Argument Clear

Usually, essays with less than one thousand words require the statement to be clearer. Remember, the length of a thesis statement should be a single sentence, which calls for clarity.

In these short essays, you do not have the freedom to write long paragraphs that provide more information on the topic of the essay.

Likewise, multiple arguments are not accommodated. This is why the thesis statement needs to be clear to inform the reader of what your essay is all about.

If you proofread your essay and notice that the thesis statement is contrary to the points you have focused on, then revise it and make sure that it incorporates the main idea of the essay. Alternatively, when the thesis statement is okay, you will have to rewrite the body of your essay.

Question your Assumptions

Before formulating a thesis statement, ask yourself the basis of the arguments presented in the thesis statement.

Assumptions are what your reader assumes to be true before accepting an argument. Before you start, it is important to be aware of the target audience of your essay.

Thinking about the ways your argument may not hold up to the people who do not subscribe to your viewpoint is crucial.

Alongside, revise the arguments that may not hold up with the people who do not subscribe to your viewpoint.

Take a Strong Stand

A thesis statement should put forward a unique perspective on what your essay is about. Avoid using observations as thesis statements.

In addition, true common facts should be avoided. Make sure that the stance you take can be supported with credible facts and valid reasons.

Equally, don’t provide a summary, make a valid argument. If the first response of the reader is “how” and “why” the thesis statement is too open-ended and not strong enough.

Make Your Thesis Statement Seen

The thesis statement should be what the reader reads at the end of the first paragraph before proceeding to the body of the essay. understanding how to write a thesis statement, leaves your objective summarized.

Positioning may sometimes vary depending on the length of the introduction that the essay requires. However, do not overthink the thesis statement. In addition, do not write it with a lot of clever twists.

Do not exaggerate the stage setting of your argument. Clever and exaggerated thesis statements are weak. Consequently, they are not clear and concise.

Good thesis statements should concentrate on one main idea. Mixing up ideas in a thesis statement makes it vague. Read on how to write an essay thesis as part of the steps to write good essays.

A reader may easily get confused about what the essay is all about if it focuses on a lot of ideas. When your ideas are related, the relation should come out more clearly.

21 Examples of Thesis Statements about Social Media

• Recently, social media is growing rapidly. Ironically, its use in remote areas has remained relatively low.
• Social media has revolutionized communication but it is evenly killing it by limiting face-to-face communication.
• Identically, social media has helped make work easier. However,at the same time it is promoting laziness and irresponsibility in society today.
• The widespread use of social media and its influence has increased desperation, anxiety, and pressure among young youths.
• Social media has made learning easier but its addiction can lead to bad grades among university students.
• As a matter of fact, social media is contributing to the downfall of mainstream media. Many advertisements and news are accessed on social media platforms today.
• Social media is a major promoter of immorality in society today with many platforms allowing sharing of inappropriate content.
• Significantly, social media promotes copycat syndrome that positively and negatively impacts the behavior adapted by different users.
• In this affluent era, social media has made life easy but consequently affects productivity and physical strength.
• The growth of social media and its ability to reach more people increases growth in today’s business world.
• The freedom on social media platforms is working against society with the recent increase in hate speech and racism.
• Lack of proper verification when signing up on social media platforms has increased the number of minors using social media exposing them to cyberbullying and inappropriate content.
• The freedom of posting anything on social media has landed many in trouble making the need to be cautious before posting anything important.
• The widespread use of social media has contributed to the rise of insecurity in urban centers
• Magazines and journals have spearheaded the appreciation of all body types but social media has increased the rate of body shaming in America.
• To stop abuse on Facebook and Twitter the owners of these social media platforms must track any abusive post and upload and ban the users from accessing the apps.
• Social media benefits marketing by creating brand recognition, increasing sales, and measuring success with analytics by tracking data.
• Social media connects people around the globe and fosters new relationships and the sharing of ideas that did not exist before its inception.
• The increased use of social media has led to the creation of business opportunities for people through social networking, particularly as social media influencers.
• Learning is convenient through social media as students can connect with education systems and learning groups that make learning convenient.
• With most people spending most of their free time glued to social media, quality time with family reduces leading to distance relationships and reduced love and closeness.

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Science News

Social media harms teens’ mental health, mounting evidence shows. what now.

Understanding what is going on in teens’ minds is necessary for targeted policy suggestions

Most teens use social media, often for hours on end. Some social scientists are confident that such use is harming their mental health. Now they want to pinpoint what explains the link.

Carol Yepes/Getty Images

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By Sujata Gupta

February 20, 2024 at 7:30 am

In January, Mark Zuckerberg, CEO of Facebook’s parent company Meta, appeared at a congressional hearing to answer questions about how social media potentially harms children. Zuckerberg opened by saying: “The existing body of scientific work has not shown a causal link between using social media and young people having worse mental health.”

But many social scientists would disagree with that statement. In recent years, studies have started to show a causal link between teen social media use and reduced well-being or mood disorders, chiefly depression and anxiety.

Ironically, one of the most cited studies into this link focused on Facebook.

Researchers delved into whether the platform’s introduction across college campuses in the mid 2000s increased symptoms associated with depression and anxiety. The answer was a clear yes , says MIT economist Alexey Makarin, a coauthor of the study, which appeared in the November 2022 American Economic Review . “There is still a lot to be explored,” Makarin says, but “[to say] there is no causal evidence that social media causes mental health issues, to that I definitely object.”

The concern, and the studies, come from statistics showing that social media use in teens ages 13 to 17 is now almost ubiquitous. Two-thirds of teens report using TikTok, and some 60 percent of teens report using Instagram or Snapchat, a 2022 survey found. (Only 30 percent said they used Facebook.) Another survey showed that girls, on average, allot roughly 3.4 hours per day to TikTok, Instagram and Facebook, compared with roughly 2.1 hours among boys. At the same time, more teens are showing signs of depression than ever, especially girls ( SN: 6/30/23 ).

As more studies show a strong link between these phenomena, some researchers are starting to shift their attention to possible mechanisms. Why does social media use seem to trigger mental health problems? Why are those effects unevenly distributed among different groups, such as girls or young adults? And can the positives of social media be teased out from the negatives to provide more targeted guidance to teens, their caregivers and policymakers?

“You can’t design good public policy if you don’t know why things are happening,” says Scott Cunningham, an economist at Baylor University in Waco, Texas.

Increasing rigor

Concerns over the effects of social media use in children have been circulating for years, resulting in a massive body of scientific literature. But those mostly correlational studies could not show if teen social media use was harming mental health or if teens with mental health problems were using more social media.

Moreover, the findings from such studies were often inconclusive, or the effects on mental health so small as to be inconsequential. In one study that received considerable media attention, psychologists Amy Orben and Andrew Przybylski combined data from three surveys to see if they could find a link between technology use, including social media, and reduced well-being. The duo gauged the well-being of over 355,000 teenagers by focusing on questions around depression, suicidal thinking and self-esteem.

Digital technology use was associated with a slight decrease in adolescent well-being , Orben, now of the University of Cambridge, and Przybylski, of the University of Oxford, reported in 2019 in Nature Human Behaviour . But the duo downplayed that finding, noting that researchers have observed similar drops in adolescent well-being associated with drinking milk, going to the movies or eating potatoes.

Holes have begun to appear in that narrative thanks to newer, more rigorous studies.

In one longitudinal study, researchers — including Orben and Przybylski — used survey data on social media use and well-being from over 17,400 teens and young adults to look at how individuals’ responses to a question gauging life satisfaction changed between 2011 and 2018. And they dug into how the responses varied by gender, age and time spent on social media.

Social media use was associated with a drop in well-being among teens during certain developmental periods, chiefly puberty and young adulthood, the team reported in 2022 in Nature Communications . That translated to lower well-being scores around ages 11 to 13 for girls and ages 14 to 15 for boys. Both groups also reported a drop in well-being around age 19. Moreover, among the older teens, the team found evidence for the Goldilocks Hypothesis: the idea that both too much and too little time spent on social media can harm mental health.

“There’s hardly any effect if you look over everybody. But if you look at specific age groups, at particularly what [Orben] calls ‘windows of sensitivity’ … you see these clear effects,” says L.J. Shrum, a consumer psychologist at HEC Paris who was not involved with this research. His review of studies related to teen social media use and mental health is forthcoming in the Journal of the Association for Consumer Research.

Cause and effect

That longitudinal study hints at causation, researchers say. But one of the clearest ways to pin down cause and effect is through natural or quasi-experiments. For these in-the-wild experiments, researchers must identify situations where the rollout of a societal “treatment” is staggered across space and time. They can then compare outcomes among members of the group who received the treatment to those still in the queue — the control group.

That was the approach Makarin and his team used in their study of Facebook. The researchers homed in on the staggered rollout of Facebook across 775 college campuses from 2004 to 2006. They combined that rollout data with student responses to the National College Health Assessment, a widely used survey of college students’ mental and physical health.

The team then sought to understand if those survey questions captured diagnosable mental health problems. Specifically, they had roughly 500 undergraduate students respond to questions both in the National College Health Assessment and in validated screening tools for depression and anxiety. They found that mental health scores on the assessment predicted scores on the screenings. That suggested that a drop in well-being on the college survey was a good proxy for a corresponding increase in diagnosable mental health disorders. 

Compared with campuses that had not yet gained access to Facebook, college campuses with Facebook experienced a 2 percentage point increase in the number of students who met the diagnostic criteria for anxiety or depression, the team found.

When it comes to showing a causal link between social media use in teens and worse mental health, “that study really is the crown jewel right now,” says Cunningham, who was not involved in that research.

A need for nuance

The social media landscape today is vastly different than the landscape of 20 years ago. Facebook is now optimized for maximum addiction, Shrum says, and other newer platforms, such as Snapchat, Instagram and TikTok, have since copied and built on those features. Paired with the ubiquity of social media in general, the negative effects on mental health may well be larger now.

Moreover, social media research tends to focus on young adults — an easier cohort to study than minors. That needs to change, Cunningham says. “Most of us are worried about our high school kids and younger.” 

And so, researchers must pivot accordingly. Crucially, simple comparisons of social media users and nonusers no longer make sense. As Orben and Przybylski’s 2022 work suggested, a teen not on social media might well feel worse than one who briefly logs on. 

Researchers must also dig into why, and under what circumstances, social media use can harm mental health, Cunningham says. Explanations for this link abound. For instance, social media is thought to crowd out other activities or increase people’s likelihood of comparing themselves unfavorably with others. But big data studies, with their reliance on existing surveys and statistical analyses, cannot address those deeper questions. “These kinds of papers, there’s nothing you can really ask … to find these plausible mechanisms,” Cunningham says.

One ongoing effort to understand social media use from this more nuanced vantage point is the SMART Schools project out of the University of Birmingham in England. Pedagogical expert Victoria Goodyear and her team are comparing mental and physical health outcomes among children who attend schools that have restricted cell phone use to those attending schools without such a policy. The researchers described the protocol of that study of 30 schools and over 1,000 students in the July BMJ Open.

Goodyear and colleagues are also combining that natural experiment with qualitative research. They met with 36 five-person focus groups each consisting of all students, all parents or all educators at six of those schools. The team hopes to learn how students use their phones during the day, how usage practices make students feel, and what the various parties think of restrictions on cell phone use during the school day.

Talking to teens and those in their orbit is the best way to get at the mechanisms by which social media influences well-being — for better or worse, Goodyear says. Moving beyond big data to this more personal approach, however, takes considerable time and effort. “Social media has increased in pace and momentum very, very quickly,” she says. “And research takes a long time to catch up with that process.”

Until that catch-up occurs, though, researchers cannot dole out much advice. “What guidance could we provide to young people, parents and schools to help maintain the positives of social media use?” Goodyear asks. “There’s not concrete evidence yet.”

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ORIGINAL RESEARCH article

Effects of social media use on psychological well-being: a mediated model.

• 1 School of Finance and Economics, Jiangsu University, Zhenjiang, China
• 2 Research Unit of Governance, Competitiveness, and Public Policies (GOVCOPP), Center for Economics and Finance (cef.up), School of Economics and Management, University of Porto, Porto, Portugal
• 3 Department of Business Administration, Sukkur Institute of Business Administration (IBA) University, Sukkur, Pakistan
• 4 CETYS Universidad, Tijuana, Mexico
• 5 Department of Business Administration, Al-Quds University, Jerusalem, Israel
• 6 Business School, Shandong University, Weihai, China

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.

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 ):

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.

Figure 1 . Conceptual model.

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.

Table 1 . 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.

Table 2 . 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 .

Table 3 . Study measures, factor loading, and the constructs' reliability and convergent validity.

Table 4 . Discriminant validity and correlation.

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 ).

Table 5 . Summary of path coefficients and hypothesis testing.

Figure 2 . Structural model.

Table 6 . Strength of the model (Predictive relevance, coefficient of determination, and model fit indices).

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.

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

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.

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

Citation: Ostic D, Qalati SA, Barbosa B, Shah SMM, Galvan Vela E, Herzallah AM and Liu F (2021) Effects of Social Media Use on Psychological Well-Being: A Mediated Model. Front. Psychol. 12:678766. doi: 10.3389/fpsyg.2021.678766

Received: 10 March 2021; Accepted: 25 May 2021; Published: 21 June 2021.

Reviewed by:

Copyright © 2021 Ostic, Qalati, Barbosa, Shah, Galvan Vela, Herzallah and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Sikandar Ali Qalati, sidqalati@gmail.com ; 5103180243@stmail.ujs.edu.cn ; Esthela Galvan Vela, esthela.galvan@cetys.mx

† 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

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How Harmful Is Social Media?

By Gideon Lewis-Kraus

In April, the social psychologist Jonathan Haidt published an essay in The Atlantic in which he sought to explain, as the piece’s title had it, “Why the Past 10 Years of American Life Have Been Uniquely Stupid.” Anyone familiar with Haidt’s work in the past half decade could have anticipated his answer: social media. Although Haidt concedes that political polarization and factional enmity long predate the rise of the platforms, and that there are plenty of other factors involved, he believes that the tools of virality—Facebook’s Like and Share buttons, Twitter’s Retweet function—have algorithmically and irrevocably corroded public life. He has determined that a great historical discontinuity can be dated with some precision to the period between 2010 and 2014, when these features became widely available on phones.

“What changed in the 2010s?” Haidt asks, reminding his audience that a former Twitter developer had once compared the Retweet button to the provision of a four-year-old with a loaded weapon. “A mean tweet doesn’t kill anyone; it is an attempt to shame or punish someone publicly while broadcasting one’s own virtue, brilliance, or tribal loyalties. It’s more a dart than a bullet, causing pain but no fatalities. Even so, from 2009 to 2012, Facebook and Twitter passed out roughly a billion dart guns globally. We’ve been shooting one another ever since.” While the right has thrived on conspiracy-mongering and misinformation, the left has turned punitive: “When everyone was issued a dart gun in the early 2010s, many left-leaning institutions began shooting themselves in the brain. And, unfortunately, those were the brains that inform, instruct, and entertain most of the country.” Haidt’s prevailing metaphor of thoroughgoing fragmentation is the story of the Tower of Babel: the rise of social media has “unwittingly dissolved the mortar of trust, belief in institutions, and shared stories that had held a large and diverse secular democracy together.”

These are, needless to say, common concerns. Chief among Haidt’s worries is that use of social media has left us particularly vulnerable to confirmation bias, or the propensity to fix upon evidence that shores up our prior beliefs. Haidt acknowledges that the extant literature on social media’s effects is large and complex, and that there is something in it for everyone. On January 6, 2021, he was on the phone with Chris Bail, a sociologist at Duke and the author of the recent book “ Breaking the Social Media Prism ,” when Bail urged him to turn on the television. Two weeks later, Haidt wrote to Bail, expressing his frustration at the way Facebook officials consistently cited the same handful of studies in their defense. He suggested that the two of them collaborate on a comprehensive literature review that they could share, as a Google Doc, with other researchers. (Haidt had experimented with such a model before.) Bail was cautious. He told me, “What I said to him was, ‘Well, you know, I’m not sure the research is going to bear out your version of the story,’ and he said, ‘Why don’t we see?’ ”

Bail emphasized that he is not a “platform-basher.” He added, “In my book, my main take is, Yes, the platforms play a role, but we are greatly exaggerating what it’s possible for them to do—how much they could change things no matter who’s at the helm at these companies—and we’re profoundly underestimating the human element, the motivation of users.” He found Haidt’s idea of a Google Doc appealing, in the way that it would produce a kind of living document that existed “somewhere between scholarship and public writing.” Haidt was eager for a forum to test his ideas. “I decided that if I was going to be writing about this—what changed in the universe, around 2014, when things got weird on campus and elsewhere—once again, I’d better be confident I’m right,” he said. “I can’t just go off my feelings and my readings of the biased literature. We all suffer from confirmation bias, and the only cure is other people who don’t share your own.”

Haidt and Bail, along with a research assistant, populated the document over the course of several weeks last year, and in November they invited about two dozen scholars to contribute. Haidt told me, of the difficulties of social-scientific methodology, “When you first approach a question, you don’t even know what it is. ‘Is social media destroying democracy, yes or no?’ That’s not a good question. You can’t answer that question. So what can you ask and answer?” As the document took on a life of its own, tractable rubrics emerged—Does social media make people angrier or more affectively polarized? Does it create political echo chambers? Does it increase the probability of violence? Does it enable foreign governments to increase political dysfunction in the United States and other democracies? Haidt continued, “It’s only after you break it up into lots of answerable questions that you see where the complexity lies.”

Haidt came away with the sense, on balance, that social media was in fact pretty bad. He was disappointed, but not surprised, that Facebook’s response to his article relied on the same three studies they’ve been reciting for years. “This is something you see with breakfast cereals,” he said, noting that a cereal company “might say, ‘Did you know we have twenty-five per cent more riboflavin than the leading brand?’ They’ll point to features where the evidence is in their favor, which distracts you from the over-all fact that your cereal tastes worse and is less healthy.”

After Haidt’s piece was published, the Google Doc—“Social Media and Political Dysfunction: A Collaborative Review”—was made available to the public . Comments piled up, and a new section was added, at the end, to include a miscellany of Twitter threads and Substack essays that appeared in response to Haidt’s interpretation of the evidence. Some colleagues and kibbitzers agreed with Haidt. But others, though they might have shared his basic intuition that something in our experience of social media was amiss, drew upon the same data set to reach less definitive conclusions, or even mildly contradictory ones. Even after the initial flurry of responses to Haidt’s article disappeared into social-media memory, the document, insofar as it captured the state of the social-media debate, remained a lively artifact.

Near the end of the collaborative project’s introduction, the authors warn, “We caution readers not to simply add up the number of studies on each side and declare one side the winner.” The document runs to more than a hundred and fifty pages, and for each question there are affirmative and dissenting studies, as well as some that indicate mixed results. According to one paper, “Political expressions on social media and the online forum were found to (a) reinforce the expressers’ partisan thought process and (b) harden their pre-existing political preferences,” but, according to another, which used data collected during the 2016 election, “Over the course of the campaign, we found media use and attitudes remained relatively stable. Our results also showed that Facebook news use was related to modest over-time spiral of depolarization. Furthermore, we found that people who use Facebook for news were more likely to view both pro- and counter-attitudinal news in each wave. Our results indicated that counter-attitudinal exposure increased over time, which resulted in depolarization.” If results like these seem incompatible, a perplexed reader is given recourse to a study that says, “Our findings indicate that political polarization on social media cannot be conceptualized as a unified phenomenon, as there are significant cross-platform differences.”

Interested in echo chambers? “Our results show that the aggregation of users in homophilic clusters dominate online interactions on Facebook and Twitter,” which seems convincing—except that, as another team has it, “We do not find evidence supporting a strong characterization of ‘echo chambers’ in which the majority of people’s sources of news are mutually exclusive and from opposite poles.” By the end of the file, the vaguely patronizing top-line recommendation against simple summation begins to make more sense. A document that originated as a bulwark against confirmation bias could, as it turned out, just as easily function as a kind of generative device to support anybody’s pet conviction. The only sane response, it seemed, was simply to throw one’s hands in the air.

When I spoke to some of the researchers whose work had been included, I found a combination of broad, visceral unease with the current situation—with the banefulness of harassment and trolling; with the opacity of the platforms; with, well, the widespread presentiment that of course social media is in many ways bad—and a contrastive sense that it might not be catastrophically bad in some of the specific ways that many of us have come to take for granted as true. This was not mere contrarianism, and there was no trace of gleeful mythbusting; the issue was important enough to get right. When I told Bail that the upshot seemed to me to be that exactly nothing was unambiguously clear, he suggested that there was at least some firm ground. He sounded a bit less apocalyptic than Haidt.

“A lot of the stories out there are just wrong,” he told me. “The political echo chamber has been massively overstated. Maybe it’s three to five per cent of people who are properly in an echo chamber.” Echo chambers, as hotboxes of confirmation bias, are counterproductive for democracy. But research indicates that most of us are actually exposed to a wider range of views on social media than we are in real life, where our social networks—in the original use of the term—are rarely heterogeneous. (Haidt told me that this was an issue on which the Google Doc changed his mind; he became convinced that echo chambers probably aren’t as widespread a problem as he’d once imagined.) And too much of a focus on our intuitions about social media’s echo-chamber effect could obscure the relevant counterfactual: a conservative might abandon Twitter only to watch more Fox News. “Stepping outside your echo chamber is supposed to make you moderate, but maybe it makes you more extreme,” Bail said. The research is inchoate and ongoing, and it’s difficult to say anything on the topic with absolute certainty. But this was, in part, Bail’s point: we ought to be less sure about the particular impacts of social media.

Bail went on, “The second story is foreign misinformation.” It’s not that misinformation doesn’t exist, or that it hasn’t had indirect effects, especially when it creates perverse incentives for the mainstream media to cover stories circulating online. Haidt also draws convincingly upon the work of Renée DiResta, the research manager at the Stanford Internet Observatory, to sketch out a potential future in which the work of shitposting has been outsourced to artificial intelligence, further polluting the informational environment. But, at least so far, very few Americans seem to suffer from consistent exposure to fake news—“probably less than two per cent of Twitter users, maybe fewer now, and for those who were it didn’t change their opinions,” Bail said. This was probably because the people likeliest to consume such spectacles were the sort of people primed to believe them in the first place. “In fact,” he said, “echo chambers might have done something to quarantine that misinformation.”

The final story that Bail wanted to discuss was the “proverbial rabbit hole, the path to algorithmic radicalization,” by which YouTube might serve a viewer increasingly extreme videos. There is some anecdotal evidence to suggest that this does happen, at least on occasion, and such anecdotes are alarming to hear. But a new working paper led by Brendan Nyhan, a political scientist at Dartmouth, found that almost all extremist content is either consumed by subscribers to the relevant channels—a sign of actual demand rather than manipulation or preference falsification—or encountered via links from external sites. It’s easy to see why we might prefer if this were not the case: algorithmic radicalization is presumably a simpler problem to solve than the fact that there are people who deliberately seek out vile content. “These are the three stories—echo chambers, foreign influence campaigns, and radicalizing recommendation algorithms—but, when you look at the literature, they’ve all been overstated.” He thought that these findings were crucial for us to assimilate, if only to help us understand that our problems may lie beyond technocratic tinkering. He explained, “Part of my interest in getting this research out there is to demonstrate that everybody is waiting for an Elon Musk to ride in and save us with an algorithm”—or, presumably, the reverse—“and it’s just not going to happen.”

When I spoke with Nyhan, he told me much the same thing: “The most credible research is way out of line with the takes.” He noted, of extremist content and misinformation, that reliable research that “measures exposure to these things finds that the people consuming this content are small minorities who have extreme views already.” The problem with the bulk of the earlier research, Nyhan told me, is that it’s almost all correlational. “Many of these studies will find polarization on social media,” he said. “But that might just be the society we live in reflected on social media!” He hastened to add, “Not that this is untroubling, and none of this is to let these companies, which are exercising a lot of power with very little scrutiny, off the hook. But a lot of the criticisms of them are very poorly founded. . . . The expansion of Internet access coincides with fifteen other trends over time, and separating them is very difficult. The lack of good data is a huge problem insofar as it lets people project their own fears into this area.” He told me, “It’s hard to weigh in on the side of ‘We don’t know, the evidence is weak,’ because those points are always going to be drowned out in our discourse. But these arguments are systematically underprovided in the public domain.”

In his Atlantic article, Haidt leans on a working paper by two social scientists, Philipp Lorenz-Spreen and Lisa Oswald, who took on a comprehensive meta-analysis of about five hundred papers and concluded that “the large majority of reported associations between digital media use and trust appear to be detrimental for democracy.” Haidt writes, “The literature is complex—some studies show benefits, particularly in less developed democracies—but the review found that, on balance, social media amplifies political polarization; foments populism, especially right-wing populism; and is associated with the spread of misinformation.” Nyhan was less convinced that the meta-analysis supported such categorical verdicts, especially once you bracketed the kinds of correlational findings that might simply mirror social and political dynamics. He told me, “If you look at their summary of studies that allow for causal inferences—it’s very mixed.”

As for the studies Nyhan considered most methodologically sound, he pointed to a 2020 article called “The Welfare Effects of Social Media,” by Hunt Allcott, Luca Braghieri, Sarah Eichmeyer, and Matthew Gentzkow. For four weeks prior to the 2018 midterm elections, the authors randomly divided a group of volunteers into two cohorts—one that continued to use Facebook as usual, and another that was paid to deactivate their accounts for that period. They found that deactivation “(i) reduced online activity, while increasing offline activities such as watching TV alone and socializing with family and friends; (ii) reduced both factual news knowledge and political polarization; (iii) increased subjective well-being; and (iv) caused a large persistent reduction in post-experiment Facebook use.” But Gentzkow reminded me that his conclusions, including that Facebook may slightly increase polarization, had to be heavily qualified: “From other kinds of evidence, I think there’s reason to think social media is not the main driver of increasing polarization over the long haul in the United States.”

In the book “ Why We’re Polarized ,” for example, Ezra Klein invokes the work of such scholars as Lilliana Mason to argue that the roots of polarization might be found in, among other factors, the political realignment and nationalization that began in the sixties, and were then sacralized, on the right, by the rise of talk radio and cable news. These dynamics have served to flatten our political identities, weakening our ability or inclination to find compromise. Insofar as some forms of social media encourage the hardening of connections between our identities and a narrow set of opinions, we might increasingly self-select into mutually incomprehensible and hostile groups; Haidt plausibly suggests that these processes are accelerated by the coalescence of social-media tribes around figures of fearful online charisma. “Social media might be more of an amplifier of other things going on rather than a major driver independently,” Gentzkow argued. “I think it takes some gymnastics to tell a story where it’s all primarily driven by social media, especially when you’re looking at different countries, and across different groups.”

Another study, led by Nejla Asimovic and Joshua Tucker, replicated Gentzkow’s approach in Bosnia and Herzegovina, and they found almost precisely the opposite results: the people who stayed on Facebook were, by the end of the study, more positively disposed to their historic out-groups. The authors’ interpretation was that ethnic groups have so little contact in Bosnia that, for some people, social media is essentially the only place where they can form positive images of one another. “To have a replication and have the signs flip like that, it’s pretty stunning,” Bail told me. “It’s a different conversation in every part of the world.”

Nyhan argued that, at least in wealthy Western countries, we might be too heavily discounting the degree to which platforms have responded to criticism: “Everyone is still operating under the view that algorithms simply maximize engagement in a short-term way” with minimal attention to potential externalities. “That might’ve been true when Zuckerberg had seven people working for him, but there are a lot of considerations that go into these rankings now.” He added, “There’s some evidence that, with reverse-chronological feeds”—streams of unwashed content, which some critics argue are less manipulative than algorithmic curation—“people get exposed to more low-quality content, so it’s another case where a very simple notion of ‘algorithms are bad’ doesn’t stand up to scrutiny. It doesn’t mean they’re good, it’s just that we don’t know.”

Bail told me that, over all, he was less confident than Haidt that the available evidence lines up clearly against the platforms. “Maybe there’s a slight majority of studies that say that social media is a net negative, at least in the West, and maybe it’s doing some good in the rest of the world.” But, he noted, “Jon will say that science has this expectation of rigor that can’t keep up with the need in the real world—that even if we don’t have the definitive study that creates the historical counterfactual that Facebook is largely responsible for polarization in the U.S., there’s still a lot pointing in that direction, and I think that’s a fair point.” He paused. “It can’t all be randomized control trials.”

Haidt comes across in conversation as searching and sincere, and, during our exchange, he paused several times to suggest that I include a quote from John Stuart Mill on the importance of good-faith debate to moral progress. In that spirit, I asked him what he thought of the argument, elaborated by some of Haidt’s critics, that the problems he described are fundamentally political, social, and economic, and that to blame social media is to search for lost keys under the streetlamp, where the light is better. He agreed that this was the steelman opponent: there were predecessors for cancel culture in de Tocqueville, and anxiety about new media that went back to the time of the printing press. “This is a perfectly reasonable hypothesis, and it’s absolutely up to the prosecution—people like me—to argue that, no, this time it’s different. But it’s a civil case! The evidential standard is not ‘beyond a reasonable doubt,’ as in a criminal case. It’s just a preponderance of the evidence.”

The way scholars weigh the testimony is subject to their disciplinary orientations. Economists and political scientists tend to believe that you can’t even begin to talk about causal dynamics without a randomized controlled trial, whereas sociologists and psychologists are more comfortable drawing inferences on a correlational basis. Haidt believes that conditions are too dire to take the hardheaded, no-reasonable-doubt view. “The preponderance of the evidence is what we use in public health. If there’s an epidemic—when COVID started, suppose all the scientists had said, ‘No, we gotta be so certain before you do anything’? We have to think about what’s actually happening, what’s likeliest to pay off.” He continued, “We have the largest epidemic ever of teen mental health, and there is no other explanation,” he said. “It is a raging public-health epidemic, and the kids themselves say Instagram did it, and we have some evidence, so is it appropriate to say, ‘Nah, you haven’t proven it’?”

This was his attitude across the board. He argued that social media seemed to aggrandize inflammatory posts and to be correlated with a rise in violence; even if only small groups were exposed to fake news, such beliefs might still proliferate in ways that were hard to measure. “In the post-Babel era, what matters is not the average but the dynamics, the contagion, the exponential amplification,” he said. “Small things can grow very quickly, so arguments that Russian disinformation didn’t matter are like COVID arguments that people coming in from China didn’t have contact with a lot of people.” Given the transformative effects of social media, Haidt insisted, it was important to act now, even in the absence of dispositive evidence. “Academic debates play out over decades and are often never resolved, whereas the social-media environment changes year by year,” he said. “We don’t have the luxury of waiting around five or ten years for literature reviews.”

Haidt could be accused of question-begging—of assuming the existence of a crisis that the research might or might not ultimately underwrite. Still, the gap between the two sides in this case might not be quite as wide as Haidt thinks. Skeptics of his strongest claims are not saying that there’s no there there. Just because the average YouTube user is unlikely to be led to Stormfront videos, Nyhan told me, doesn’t mean we shouldn’t worry that some people are watching Stormfront videos; just because echo chambers and foreign misinformation seem to have had effects only at the margins, Gentzkow said, doesn’t mean they’re entirely irrelevant. “There are many questions here where the thing we as researchers are interested in is how social media affects the average person,” Gentzkow told me. “There’s a different set of questions where all you need is a small number of people to change—questions about ethnic violence in Bangladesh or Sri Lanka, people on YouTube mobilized to do mass shootings. Much of the evidence broadly makes me skeptical that the average effects are as big as the public discussion thinks they are, but I also think there are cases where a small number of people with very extreme views are able to find each other and connect and act.” He added, “That’s where many of the things I’d be most concerned about lie.”

The same might be said about any phenomenon where the base rate is very low but the stakes are very high, such as teen suicide. “It’s another case where those rare edge cases in terms of total social harm may be enormous. You don’t need many teen-age kids to decide to kill themselves or have serious mental-health outcomes in order for the social harm to be really big.” He added, “Almost none of this work is able to get at those edge-case effects, and we have to be careful that if we do establish that the average effect of something is zero, or small, that it doesn’t mean we shouldn’t be worried about it—because we might be missing those extremes.” Jaime Settle, a scholar of political behavior at the College of William & Mary and the author of the book “ Frenemies: How Social Media Polarizes America ,” noted that Haidt is “farther along the spectrum of what most academics who study this stuff are going to say we have strong evidence for.” But she understood his impulse: “We do have serious problems, and I’m glad Jon wrote the piece, and down the road I wouldn’t be surprised if we got a fuller handle on the role of social media in all of this—there are definitely ways in which social media has changed our politics for the worse.”

It’s tempting to sidestep the question of diagnosis entirely, and to evaluate Haidt’s essay not on the basis of predictive accuracy—whether social media will lead to the destruction of American democracy—but as a set of proposals for what we might do better. If he is wrong, how much damage are his prescriptions likely to do? Haidt, to his great credit, does not indulge in any wishful thinking, and if his diagnosis is largely technological his prescriptions are sociopolitical. Two of his three major suggestions seem useful and have nothing to do with social media: he thinks that we should end closed primaries and that children should be given wide latitude for unsupervised play. His recommendations for social-media reform are, for the most part, uncontroversial: he believes that preteens shouldn’t be on Instagram and that platforms should share their data with outside researchers—proposals that are both likely to be beneficial and not very costly.

It remains possible, however, that the true costs of social-media anxieties are harder to tabulate. Gentzkow told me that, for the period between 2016 and 2020, the direct effects of misinformation were difficult to discern. “But it might have had a much larger effect because we got so worried about it—a broader impact on trust,” he said. “Even if not that many people were exposed, the narrative that the world is full of fake news, and you can’t trust anything, and other people are being misled about it—well, that might have had a bigger impact than the content itself.” Nyhan had a similar reaction. “There are genuine questions that are really important, but there’s a kind of opportunity cost that is missed here. There’s so much focus on sweeping claims that aren’t actionable, or unfounded claims we can contradict with data, that are crowding out the harms we can demonstrate, and the things we can test, that could make social media better.” He added, “We’re years into this, and we’re still having an uninformed conversation about social media. It’s totally wild.”

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How Does Social Media Affect Your Mental Health?

Facebook has delayed the development of an Instagram app for children amid questions about its harmful effects on young people’s mental health. Does social media have an impact on your well-being?

By Nicole Daniels

What is your relationship with social media like? Which platforms do you spend the most time on? Which do you stay away from? How often do you log on?

What do you notice about your mental health and well-being when spending time on social networks?

In “ Facebook Delays Instagram App for Users 13 and Younger ,” Adam Satariano and Ryan Mac write about the findings of an internal study conducted by Facebook and what they mean for the Instagram Kids app that the company was developing:

Facebook said on Monday that it had paused development of an Instagram Kids service that would be tailored for children 13 years old or younger, as the social network increasingly faces questions about the app’s effect on young people’s mental health. The pullback preceded a congressional hearing this week about internal research conducted by Facebook , and reported in The Wall Street Journal , that showed the company knew of the harmful mental health effects that Instagram was having on teenage girls. The revelations have set off a public relations crisis for the Silicon Valley company and led to a fresh round of calls for new regulation. Facebook said it still wanted to build an Instagram product intended for children that would have a more “age appropriate experience,” but was postponing the plans in the face of criticism.

The article continues:

With Instagram Kids, Facebook had argued that young people were using the photo-sharing app anyway, despite age-requirement rules, so it would be better to develop a version more suitable for them. Facebook said the “kids” app was intended for ages 10 to 12 and would require parental permission to join, forgo ads and carry more age-appropriate content and features. Parents would be able to control what accounts their child followed. YouTube, which Google owns, has released a children’s version of its app. But since BuzzFeed broke the news this year that Facebook was working on the app, the company has faced scrutiny. Policymakers, regulators, child safety groups and consumer rights groups have argued that it hooks children on the app at a younger age rather than protecting them from problems with the service, including child predatory grooming, bullying and body shaming.

The article goes on to quote Adam Mosseri, the head of Instagram:

Mr. Mosseri said on Monday that the “the project leaked way before we knew what it would be” and that the company had “few answers” for the public at the time. Opposition to Facebook’s plans gained momentum this month when The Journal published articles based on leaked internal documents that showed Facebook knew about many of the harms it was causing. Facebook’s internal research showed that Instagram, in particular, had caused teen girls to feel worse about their bodies and led to increased rates of anxiety and depression, even while company executives publicly tried to minimize the app’s downsides.

But concerns about the effect of social media on young people go beyond Instagram Kids, the article notes:

A children’s version of Instagram would not fix more systemic problems, said Al Mik, a spokesman for 5Rights Foundation, a London group focused on digital rights issues for children. The group published a report in July showing that children as young as 13 were targeted within 24 hours of creating an account with harmful content, including material related to eating disorders, extreme diets, sexualized imagery, body shaming, self-harm and suicide. “Big Tobacco understood that the younger you got to someone, the easier you could get them addicted to become a lifelong user,” Doug Peterson, Nebraska’s attorney general, said in an interview. “I see some comparisons to social media platforms.” In May, attorneys general from 44 states and jurisdictions had signed a letter to Facebook’s chief executive, Mark Zuckerberg, asking him to end plans for building an Instagram app for children. American policymakers should pass tougher laws to restrict how tech platforms target children, said Josh Golin, executive director of Fairplay, a Boston-based group that was part of an international coalition of children’s and consumer groups opposed to the new app. Last year, Britain adopted an Age Appropriate Design Code , which requires added privacy protections for digital services used by people under the age of 18.

Students, read the entire article , then tell us:

Do you think Facebook made the right decision in halting the development of the Instagram Kids app? Do you think there should be social media apps for children 13 and younger? Why or why not?

What is your reaction to the research that found that Instagram can have harmful mental health effects on teenagers, particularly teenage girls? Have you experienced body image issues, anxiety or depression tied to your use of the app? How do you think social media affects your mental health?

What has your experience been on different social media apps? Are there apps that have a more positive or negative effect on your well-being? What do you think could explain these differences?

Have you ever been targeted with inappropriate or harmful content on Instagram or other social media apps? What responsibility do you think social media companies have to address these issues? Do you think there should be more protections in place for users under 18? Why or why not?

What does healthy social media engagement look like for you? What habits do you have around social media that you feel proud of? What behaviors would you like to change? How involved are your parents in your social media use? How involved do you think they should be?

If you were in charge of making Instagram, or another social media app, safer for teenagers, what changes would you make?

Want more writing prompts? You can find all of our questions in our Student Opinion column . Teachers, check out this guide to learn how you can incorporate them into your classroom.

Students 13 and older in the United States and Britain, and 16 and older elsewhere, are invited to comment. All comments are moderated by the Learning Network staff, but please keep in mind that once your comment is accepted, it will be made public.

Nicole Daniels joined The Learning Network as a staff editor in 2019 after working in museum education, curriculum writing and bilingual education. More about Nicole Daniels

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Just How Harmful Is Social Media? Our Experts Weigh-In.

A recent investigation by the Wall Street Journal revealed that Facebook was aware of mental health risks linked to the use of its Instagram app but kept those findings secret. Internal research by the social media giant found that Instagram worsened body image issues for one in three teenage girls, and all teenage users of the app linked it to experiences of anxiety and depression. It isn’t the first evidence of social media’s harms. Watchdog groups have identified Facebook and Instagram as avenues for cyberbullying , and reports have linked TikTok to dangerous and antisocial behavior, including a recent spate of school vandalism .

As social media has proliferated worldwide—Facebook has 2.85 billion users—so too have concerns over how the platforms are affecting individual and collective wellbeing. Social media is criticized for being addictive by design and for its role in the spread of misinformation on critical issues from vaccine safety to election integrity, as well as the rise of right-wing extremism. Social media companies, and many users, defend the platforms as avenues for promoting creativity and community-building. And some research has pushed back against the idea that social media raises the risk for depression in teens . So just how healthy or unhealthy is social media?

Two experts from Columbia University Mailman School of Public Health and Columbia Psychiatry share their insights into one crucial aspect of social media’s influence—its effect on the mental health of young people and adults. Deborah Glasofer , associate professor of psychology in psychiatry, conducts psychotherapy development research for adults with eating disorders and teaches about cognitive behavioral therapy. She is the co-author of the book Eating Disorders: What Everyone Needs to Know. Claude Mellins , Professor of medical psychology in the Departments of Psychiatry and Sociomedical Sciences, studies wellbeing among college and graduate students, among other topics, and serves as program director of CopeColumbia, a peer support program for Columbia faculty and staff whose mental health has been affected by the COVID-19 pandemic. She co-led the SHIFT research study to reduce sexual violence among undergraduates. Both use social media.

What do we know about the mental health risks of social media use?

Mellins : Facebook and Instagram and other social media platforms are important sources of socialization and relationship-building for many young people. Although there are important benefits, social media can also provide platforms for bullying and exclusion, unrealistic expectations about body image and sources of popularity, normalization of risk-taking behaviors, and can be detrimental to mental health. Girls and young people who identify as sexual and gender minorities can be especially vulnerable as targets. Young people’s brains are still developing, and as individuals, young people are developing their own identities. What they see on social media can define what is expected in ways that is not accurate and that can be destructive to identity development and self-image. Adolescence is a time of risk-taking, which is both a strength and a vulnerability. Social media can exacerbate risks, as we have seen played out in the news. 

Although there are important benefits, social media can also provide platforms for bullying and exclusion, unrealistic expectations about body image and sources of popularity, normalization of risk-taking behaviors, and can be detrimental to mental health. – Claude Mellins

Glasofer : For those vulnerable to developing an eating disorder, social media may be especially unhelpful because it allows people to easily compare their appearance to their friends, to celebrities, even older images of themselves. Research tells us that how much someone engages with photo-related activities like posting and sharing photos on Facebook or Instagram is associated with less body acceptance and more obsessing about appearance. For adolescent girls in particular, the more time they spend on social media directly relates to how much they absorb the idea that being thin is ideal, are driven to try to become thin, and/or overly scrutinize their own bodies. Also, if someone is vulnerable to an eating disorder, they may be especially attracted to seeking out unhelpful information—which is all too easy to find on social media.

Are there any upsides to social media?

Mellins : For young people, social media provides a platform to help them figure out who they are. For very shy or introverted young people, it can be a way to meet others with similar interests. During the pandemic, social media made it possible for people to connect in ways when in-person socialization was not possible.  Social support and socializing are critical influences on coping and resilience. Friends we couldn’t see in person were available online and allowed us important points of connection. On the other hand, fewer opportunities for in-person interactions with friends and family meant less of a real-world check on some of the negative influences of social media.

Whether it’s social media or in person, a good peer group makes the difference. A group of friends that connects over shared interests like art or music, and is balanced in their outlook on eating and appearance, is a positive. – Deborah Glasofer

Glasofer : Whether it’s social media or in person, a good peer group makes the difference. A group of friends that connects over shared interests like art or music, and is balanced in their outlook on eating and appearance, is a positive. In fact, a good peer group online may be protective against negative in-person influences. For those with a history of eating disorders, there are body-positive and recovery groups on social media. Some people find these groups to be supportive; for others, it’s more beneficial to move on and pursue other interests.

Is there a healthy way to be on social media?

Mellins : If you feel social media is a negative experience, you might need a break. Disengaging with social media permanently is more difficult­—especially for young people. These platforms are powerful tools for connecting and staying up-to-date with friends and family. Social events, too. If you’re not on social media then you’re reliant on your friends to reach out to you personally, which doesn’t always happen. It’s complicated.

Glasofer : When you find yourself feeling badly about yourself in relation to what other people are posting about themselves, then social media is not doing you any favors. If there is anything on social media that is negatively affecting your actions or your choices­—for example, if you’re starting to eat restrictively or exercise excessively—then it’s time to reassess. Parents should check-in with their kids about their lives on social media. In general, I recommend limiting social media— creating boundaries that are reasonable and work for you—so you can be present with people in your life. I also recommend social media vacations. It’s good to take the time to notice the difference between the virtual world and the real world.

• Systematic Review
• Open access
• Published: 12 May 2024

Association between problematic social networking use and anxiety symptoms: a systematic review and meta-analysis

• Mingxuan Du 1 ,
• Chengjia Zhao 2 ,
• Haiyan Hu 1 ,
• Ningning Ding 1 ,
• Jiankang He 1 ,
• Wenwen Tian 1 ,
• Wenqian Zhao 1 ,
• Xiujian Lin 1 ,
• Gaoyang Liu 1 ,
• Wendan Chen 1 ,
• ShuangLiu Wang 1 ,
• Pengcheng Wang 3 ,
• Dongwu Xu 1 ,
• Xinhua Shen 4 &
• Guohua Zhang 1  

BMC Psychology volume  12 , Article number:  263 ( 2024 ) Cite this article

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Metrics details

A growing number of studies have reported that problematic social networking use (PSNU) is strongly associated with anxiety symptoms. However, due to the presence of multiple anxiety subtypes, existing research findings on the extent of this association vary widely, leading to a lack of consensus. The current meta-analysis aimed to summarize studies exploring the relationship between PSNU levels and anxiety symptoms, including generalized anxiety, social anxiety, attachment anxiety, and fear of missing out. 209 studies with a total of 172 articles were included in the meta-analysis, involving 252,337 participants from 28 countries. The results showed a moderately positive association between PSNU and generalized anxiety (GA), social anxiety (SA), attachment anxiety (AA), and fear of missing out (FoMO) respectively (GA: r  = 0.388, 95% CI [0.362, 0.413]; SA: r  = 0.437, 95% CI [0.395, 0.478]; AA: r  = 0.345, 95% CI [0.286, 0.402]; FoMO: r  = 0.496, 95% CI [0.461, 0.529]), and there were different regulatory factors between PSNU and different anxiety subtypes. This study provides the first comprehensive estimate of the association of PSNU with multiple anxiety subtypes, which vary by time of measurement, region, gender, and measurement tool.

Peer Review reports

Introduction

Social network refers to online platforms that allow users to create, share, and exchange information, encompassing text, images, audio, and video [ 1 ]. The use of social network, a term encompassing various activities on these platforms, has been measured from angles such as frequency, duration, intensity, and addictive behavior, all indicative of the extent of social networking usage [ 2 ]. As of April 2023, there are 4.8 billion social network users globally, representing 59.9% of the world’s population [ 3 ]. The usage of social network is considered a normal behavior and a part of everyday life [ 4 , 5 ]. Although social network offers convenience in daily life, excessive use can lead to PSNU [ 6 , 7 ], posing potential threats to mental health, particularly anxiety symptoms (Rasmussen et al., 2020). Empirical research has shown that anxiety symptoms, including generalized anxiety (GA), social anxiety (SA), attachment anxiety (AA), and fear of missing out (FoMO), are closely related to PSNU [ 8 , 9 , 10 , 11 , 12 ]. While some empirical studies have explored the relationship between PSNU and anxiety symptoms, their conclusions are not consistent. Some studies have found a significant positive correlation [ 13 , 14 , 15 ], while others have found no significant correlation [ 16 , 17 , 18 , 19 ]. Furthermore, the degree of correlation varies widely in existing research, with reported r-values ranging from 0.12 to 0.80 [ 20 , 21 ]. Therefore, a systematic meta-analysis is necessary to clarify the impact of PSNU on individual anxiety symptoms.

Previous research lacks a unified concept of PSNU, primarily due to differing theoretical interpretations by various authors, and the use of varied standards and diagnostic tools. Currently, this phenomenon is referred to by several terms, including compulsive social networking use, problematic social networking use, excessive social networking use, social networking dependency, and social networking addiction [ 22 , 23 , 24 , 25 , 26 ]. These conceptual differences hinder the development of a cohesive and systematic research framework, as it remains unclear whether these definitions and tools capture the same underlying construct [ 27 ]. To address this lack of uniformity, this paper will use the term “problematic use” to encompass all the aforementioned nomenclatures (i.e., compulsive, excessive, dependent, and addictive use).

Regarding the relationship between PSNU and anxiety symptoms, two main perspectives exist: the first suggests a positive correlation, while the second proposes a U-shaped relationship. The former perspective, advocating a positive correlation, aligns with the social cognitive theory of mass communication. It posits that PSNU can reinforce certain cognitions, emotions, attitudes, and behaviors [ 28 , 29 ], potentially elevating individuals’ anxiety levels [ 30 ]. Additionally, the cognitive-behavioral model of pathological use, a primary framework for explaining factors related to internet-based addictions, indicates that psychiatric symptoms like depression or anxiety may precede internet addiction, implying that individuals experiencing anxiety may turn to social networking platforms as a coping mechanism [ 31 ]. Empirical research also suggests that highly anxious individuals prefer computer-mediated communication due to the control and social liberation it offers and are more likely to have maladaptive emotional regulation, potentially leading to problematic social network service use [ 32 ]. Turning to the alternate perspective, it proposes a U-shaped relationship as per the digital Goldilocks hypothesis. In this view, moderate social networking usage is considered beneficial for psychosocial adaptation, providing individuals with opportunities for social connection and support. Conversely, both excessive use and abstinence can negatively impact psychosocial adaptation [ 33 ]. In summary, both perspectives offer plausible explanations.

Incorporating findings from previous meta-analyses, we identified seven systematic reviews and two meta-analyses that investigated the association between PSNU and anxiety. The results of these meta-analyses indicated a significant positive correlation between PSNU and anxiety (ranging from 0.33 to 0.38). However, it is evident that these previous meta-analyses had certain limitations. Firstly, they focused only on specific subtypes of anxiety; secondly, they were limited to adolescents and emerging adults in terms of age. In summary, this systematic review aims to ascertain which theoretical perspective more effectively explains the relationship between PSNU and anxiety, addressing the gaps in previous meta-analyses. Additionally, the association between PSNU and anxiety could be moderated by various factors. Drawing from a broad research perspective, any individual study is influenced by researcher-specific designs and associated sample estimates. These may lead to bias compared to the broader population. Considering the selection criteria for moderating variables in empirical studies and meta-analyses [ 34 , 35 ], the heterogeneity of findings on problematic social network usage and anxiety symptoms could be driven by divergence in sample characteristics (e.g., gender, age, region) and research characteristics (measurement instrument of study variables). Since the 2019 coronavirus pandemic, heightened public anxiety may be attributed to the fear of the virus or heightened real life stress. The increased use of electronic devices, particularly smartphones during the pandemic, also instigates the prevalence of problematic social networking. Thus, our analysis focuses on three moderators: sample characteristics (participants’ gender, age, region), measurement tools (for PSNU and anxiety symptoms) and the time of measurement (before COVID-19 vs. during COVID-19).

The present study was conducted in accordance with the 2020 statement on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [ 36 ]. To facilitate transparency and to avoid unnecessary duplication of research, this study was registered on PROSPERO, and the number is CRD42022350902.

Literature search

Studies on the relationship between the PSNU and anxiety symptoms from 2000 to 2023 were retrieved from seven databases. These databases included China National Knowledge Infrastructure (CNKI), Wanfang Data, Chongqing VIP Information Co. Ltd. (VIP), Web of Science, ScienceDirect, PubMed, and PsycARTICLES. The search strings consisted of (a) anxiety symptoms, (b) social network, and (c) Problematic use. As shown in Table  1 , the keywords for anxiety are as follows: anxiety, generalized anxiety, social anxiety, attachment anxiety, fear of missing out, and FoMO. The keywords for social network are as follows: social network, social media, social networking site, Instagram, and Facebook. The keywords for addiction are as follows: addiction, dependence, problem/problematic use, excessive use. The search deadline was March 19, 2023. A total of 2078 studies were initially retrieved and all were identified ultimately.

Inclusion and exclusion criteria

Retrieved studies were eligible for the present meta-analysis if they met the following inclusion criteria: (a) the study provided Pearson correlation coefficients used to measure the relationship between PSNU and anxiety symptoms; (b) the study reported the sample size and the measurement instruments for the variables; (c) the study was written in English and Chinese; (d) the study provided sufficient statistics to calculate the effect sizes; (e) effect sizes were extracted from independent samples. If multiple independent samples were investigated in the same study, they were coded separately; if the study was a longitudinal study, they were coded by the first measurement. In addition, studies were excluded if they: (a) examined non-problematic social network use; (b) had an abnormal sample population; (c) the results of the same sample were included in another study and (d) were case reports or review articles. Two evaluators with master’s degrees independently assessed the eligibility of the articles. A third evaluator with a PhD examined the results and resolved dissenting views.

Data extraction and quality assessment

Two evaluators independently coded the selected articles according to the following characteristics: literature information, time of measurement (before the COVID-19 vs. during the COVID-19), sample source (developed country vs. developing country), sample size, proportion of males, mean age, type of anxiety, and measurement instruments for PSNU and anxiety symptoms. The following principles needed to be adhered to in the coding process: (a) effect sizes were extracted from independent samples. If multiple independent samples were investigated in the same study, they were coded separately; if the study was a longitudinal study, it was coded by the first measurement; (b) if multiple studies used the same data, the one with the most complete information was selected; (c) If studies reported t or F values rather than r , the following formula \( r=\sqrt{\frac{{t}^{2}}{{t}^{2}+df}}\) ; \( r=\sqrt{\frac{F}{F+d{f}_{e}}}\) was used to convert them into r values [ 37 , 38 ]. Additionally, if some studies only reported the correlation matrix between each dimension of PSNU and anxiety symptoms, the following formula \( {r}_{xy}=\frac{\sum {r}_{xi}{r}_{yj}}{\sqrt{n+n(n-1){r}_{xixj}}\sqrt{m+m(m-1){r}_{yiyj}}}\) was used to synthesize the r values [ 39 ], where n or m is the number of dimensions of variable x or variable y, respectively, and \( {r}_{xixj} \) or \( {r}_{yiyj}\) represents the mean of the correlation coefficients between the dimensions of variable x or variable y, respectively.

Literature quality was determined according to the meta-analysis quality evaluation scale developed [ 40 ]. The quality of the post-screening studies was assessed by five dimensions: sampling method, efficiency of sample collection, level of publication, and reliability of PSNU and anxiety symptom measurement instruments. The total score of the scale ranged from 0 to 10; higher scores indicated better quality of the literature.

Data analysis

All data were performed using Comprehensive Meta Analysis 3.3 (CMA 3.3). Pearson’s product-moment coefficient r was selected as the effect size index in this meta-analysis. Firstly, \( {\text{F}\text{i}\text{s}\text{h}\text{e}\text{r}}^{{\prime }}\text{s} Z=\frac{1}{2}\times \text{ln}\left(\frac{1+r}{1-r}\right)\) was used to convert the correlation coefficient to Fisher Z . Then the formula \( SE=\sqrt{\frac{1}{n-3}}\) was used to calculate the standard error ( SE ). Finally, the summary of r was obtained from the formula \( r=\frac{{e}^{2z}-1}{{e}^{2z}+1}\) for a comprehensive measure of the relationship between PSNU and anxiety symptoms [ 37 , 41 ].

Although the effect sizes estimated by the included studies may be similar, considering the actual differences between studies (e.g., region and gender), the random effects model was a better choice for data analysis for the current meta-analysis. The heterogeneity of the included study effect sizes was measured for significance by Cochran’s Q test and estimated quantitatively by the I 2 statistic [ 42 ]. If the results indicate there is a significant heterogeneity (the Q test: p -value < 0.05, I 2  > 75) and the results of different studies are significantly different from the overall effect size. Conversely, it indicates there are no differences between the studies and the overall effect size. And significant heterogeneity tends to indicate the possible presence of potential moderating variables. Subgroup analysis and meta-regression analysis were used to examine the moderating effect of categorical and continuous variables, respectively.

Funnel plots, fail-safe number (Nfs) and Egger linear regression were utilized to evaluate the publication bias [ 43 , 44 , 45 ]. The likelihood of publication bias was considered low if the intercept obtained from Egger linear regression was not significant. A larger Nfs indicated a lower risk of publication bias, and if Nfs < 5k + 10 (k representing the original number of studies), publication bias should be a concern [ 46 ]. When Egger’s linear regression was significant, the Duval and Tweedie’s trim-and-fill was performed to correct the effect size. If there was no significant change in the effect size, it was assumed that there was no serious publication bias [ 47 ].

A significance level of P  < 0.05 was deemed applicable in this study.

Sample characteristics

The PRISMA search process is depicted in Fig.  1 . The database search yielded 2078 records. After removing duplicate records and screening the title and abstract, the full text was subject to further evaluation. Ultimately, 172 records fit the inclusion criteria, including 209 independent effect sizes. The present meta-analysis included 68 studies on generalized anxiety, 44 on social anxiety, 22 on attachment anxiety, and 75 on fear of missing out. The characteristics of the selected studies are summarized in Table  2 . The majority of the sample group were adults. Quality scores for selected studies ranged from 0 to 10, with only 34 effect sizes below the theoretical mean, indicating high quality for the included studies. The literature included utilized BSMAS as the primary tool to measure PSNU, DASS-21-A to measure GA, IAS to measure SA, ECR to measure AA, and FoMOS to measure FoMO.

Flow chart of the search and selection strategy

Overall analysis, homogeneity tests and publication bias

As shown in Table  3 , there was significant heterogeneity between PSNU and all four anxiety symptoms (GA: Q  = 1623.090, I 2  = 95.872%; SA: Q  = 1396.828, I 2  = 96.922%; AA: Q  = 264.899, I 2  = 92.072%; FoMO: Q  = 1847.110, I 2  = 95.994%), so a random effects model was chosen. The results of the random effects model indicate a moderate positive correlation between PSNU and anxiety symptoms (GA: r  = 0.350, 95% CI [0.323, 0.378]; SA: r  = 0.390, 95% CI [0.347, 0.431]; AA: r  = 0.345, 95% CI [0.286, 0.402]; FoMO: r  = 0.496, 95% CI [0.461, 0.529]).

Figure  2 shows the funnel plot of the relationship between PSNU and anxiety symptoms. No significant symmetry was seen in the funnel plot of the relationship between PSNU and GA and between PSNU and SA. And the Egger’s regression results also indicated that there might be publication bias ( t  = 3.775, p  < 0.001; t  = 2.309, p  < 0.05). Therefore, it was necessary to use fail-safe number (Nfs) and the trim and fill method for further examination and correction. The Nfs for PSNU and GA as well as PSNU and SA are 4591 and 7568, respectively. Both Nfs were much larger than the standard 5 k  + 10. After performing the trim and fill method, 14 effect sizes were added to the right side of the funnel plat (Fig.  2 .a), the correlation coefficient between PSNU and GA changed to ( r  = 0.388, 95% CI [0.362, 0.413]); 10 effect sizes were added to the right side of the funnel plat (Fig.  2 .b), the correlation coefficient between PSNU and SA changed to ( r  = 0.437, 95% CI [0.395, 0.478]). The correlation coefficients did not change significantly, indicating that there was no significant publication bias associated with the relationship between PSNU and these two anxiety symptoms (GA and SA).

Funnel plot of the relationship between PSNU and anxiety symptoms. Note: Black dots indicated additional studies after using trim and fill method; ( a ) = Funnel plot of the PSNU and GA; ( b ) = Funnel plot of the PSNU and SA; ( c ) = Funnel plot of the PSNU and AA; ( d ) = Funnel plot of the PSNU and FoMO

Sensitivity analyses

Initially, the findings obtained through the one-study-removed approach indicated that the heterogeneities in the relationship between PSNU and anxiety symptoms were not attributed to any individual study. Nevertheless, it is important to note that sensitivity analysis should be performed based on literature quality [ 223 ] since low-quality literature could potentially impact result stability. In the relationship between PSNU and GA, the 10 effect sizes below the theoretical mean scores were excluded from analysis, and the sensitivity analysis results were recalculated ( r  = 0.402, 95% CI [0.375, 0.428]); In the relationship between PSNU and SA, the 8 effect sizes below the theoretical mean scores were excluded from analysis, and the sensitivity analysis results were recalculated ( r  = 0.431, 95% CI [0.387, 0.472]); In the relationship between PSNU and AA, the 5 effect sizes below the theoretical mean scores were excluded from analysis, and the sensitivity analysis results were recalculated ( r  = 0.367, 95% CI [0.298, 0.433]); In the relationship between PSNU and FoMO, the 11 effect sizes below the theoretical mean scores were excluded from analysis, and the sensitivity analysis results were recalculated ( r  = 0.508, 95% CI [0.470, 0.544]). The revised estimates indicate that meta-analysis results were stable.

Moderator analysis

The impact of moderator variables on the relation between psnu and ga.

The results of subgroup analysis and meta-regression are shown in Table  4 , the time of measurement significantly moderated the correlation between PSNU and GA ( Q between = 19.268, df  = 2, p  < 0.001). The relation between the two variables was significantly higher during the COVID-19 ( r  = 0.392, 95% CI [0.357, 0.425]) than before the COVID-19 ( r  = 0.270, 95% CI [0.227, 0.313]) or measurement time uncertain ( r  = 0.352, 95% CI [0.285, 0.415]).

The moderating effect of the PSNU measurement was significant ( Q between = 6.852, df  = 1, p  = 0.009). The relation was significantly higher when PSNU was measured with the BSMAS ( r  = 0.373, 95% CI [0.341, 0.404]) compared to others ( r  = 0.301, 95% CI [0.256, 0.344]).

The moderating effect of the GA measurement was significant ( Q between = 60.061, df  = 5, p  < 0.001). Specifically, when GA measured by the GAD ( r  = 0.398, 95% CI [0.356, 0.438]) and the DASS-21-A ( r  = 0.433, 95% CI [0.389, 0.475]), a moderate positive correlation was observed. However, the correlation was less significant when measured using the STAI ( r  = 0.232, 95% CI [0.187, 0.276]).

For the relation between PSNU and GA, the moderating effect of region, gender and age were not significant.

The impact of moderator variables on the relation between PSNU and SA

The effects of the moderating variables in the relation between PSNU and SA were shown in Table  5 . The results revealed a gender-moderated variances between the two variables (b = 0.601, 95% CI [ 0.041, 1.161], Q model (1, k = 41) = 4.705, p  = 0.036).

For the relation between PSNU and SA, the moderating effects of time of measurement, region, measurement of PSNU and SA, and age were not significant.

The impact of moderator variables on the relation between PSNU and AA

The effects of the moderating variables in the relation between PSNU and AA were shown in Table  6 , region significantly moderated the correlation between PSNU and AA ( Q between = 6.410, df  = 2, p  = 0.041). The correlation between the two variables was significantly higher in developing country ( r  = 0.378, 95% CI [0.304, 0.448]) than in developed country ( r  = 0.242, 95% CI [0.162, 0.319]).

The moderating effect of the PSNU measurement was significant ( Q between = 6.852, df  = 1, p  = 0.009). Specifically, when AA was measured by the GPIUS-2 ( r  = 0.484, 95% CI [0.200, 0.692]) and the PMSMUAQ ( r  = 0.443, 95% CI [0.381, 0.501]), a moderate positive correlation was observed. However, the correlation was less significant when measured using the BSMAS ( r  = 0.248, 95% CI [0.161, 0.331]) and others ( r  = 0.313, 95% CI [0.250, 0.372]).

The moderating effect of the AA measurement was significant ( Q between = 17.283, df  = 2, p  < 0.001). The correlation was significantly higher when measured using the ECR ( r  = 0.386, 95% CI [0.338, 0.432]) compared to the RQ ( r  = 0.200, 95% CI [0.123, 0.275]).

For the relation between PSNU and AA, the moderating effects of time of measurement, region, gender, and age were not significant.

The impact of moderator variables on the relation between PSNU and FoMO

The effects of the moderating variables in the relation between PSNU and FoMO were shown in Table  7 , the moderating effect of the PSNU measurement was significant ( Q between = 8.170, df  = 2, p  = 0.017). Among the sub-dimensions, the others was excluded because there was only one sample. Specifically, when measured using the FoMOS-MSME ( r  = 0.630, 95% CI [0.513, 0.725]), a moderate positive correlation was observed. However, the correlation was less significant when measured using the FoMOS ( r  = 0.472, 95% CI [0.432, 0.509]) and the T-S FoMOS ( r  = 0.557, 95% CI [0.463, 0.639]).

For the relationship between PSNU and FoMO, the moderating effects of time of measurement, region, measurement of PSNU, gender and age were not significant.

Through systematic review and meta-analysis, this study established a positive correlation between PSNU and anxiety symptoms (i.e., generalized anxiety, social anxiety, attachment anxiety, and fear of missing out), confirming a linear relationship and partially supporting the Social Cognitive Theory of Mass Communication [ 28 ] and the Cognitive Behavioral Model of Pathological Use [ 31 ]. Specifically, a significant positive correlation between PSNU and GA was observed, implying that GA sufferers might resort to social network for validation or as an escape from reality, potentially alleviating their anxiety. Similarly, the meta-analysis demonstrated a strong positive correlation between PSNU and SA, suggesting a preference for computer-mediated communication among those with high social anxiety due to perceived control and liberation offered by social network. This preference is often accompanied by maladaptive emotional regulation, predisposing them to problematic use. In AA, a robust positive correlation was found with PSNU, indicating a higher propensity for such use among individuals with attachment anxiety. Notably, the study identified the strongest correlation in the context of FoMO. FoMO’s significant association with PSNU is multifaceted, stemming from the real-time nature of social networks that engenders a continuous concern about missing crucial updates or events. This drives frequent engagement with social network, thereby establishing a direct link to problematic usage patterns. Additionally, social network’s feedback loops amplify this effect, intensifying FoMO. The culture of social comparison on these platforms further exacerbates FoMO, as users frequently compare their lives with others’ selectively curated portrayals, enhancing both their social networking usage frequency and the pursuit for social validation. Furthermore, the integral role of social network in modern life broadens FoMO’s scope, encompassing anxieties about staying informed and connected.

The notable correlation between FoMO and PSNU can be comprehensively understood through various perspectives. FoMO is inherently linked to the real-time nature of social networks, which cultivates an ongoing concern about missing significant updates or events in one’s social circle [ 221 ]. This anxiety prompts frequent engagement with social network, leading to patterns of problematic use. Moreover, the feedback loops in social network algorithms, designed to enhance user engagement, further intensify this fear [ 224 ]. Additionally, social comparison, a common phenomenon on these platforms, exacerbates FoMO as users continuously compare their lives with the idealized representations of others, amplifying feelings of missing out on key social experiences [ 225 ]. This behavior not only increases social networking usage but also is closely linked to the quest for social validation and identity construction on these platforms. The extensive role of social network in modern life further amplifies FoMO, as these platforms are crucial for information exchange and maintaining social ties. FoMO thus encompasses more than social concerns, extending to anxieties about staying informed with trends and dynamics within social networks [ 226 ]. The multifaceted nature of FoMO in relation to social network underscores its pronounced correlation with problematic social networking usage. In essence, the combination of social network’s intrinsic characteristics, psychological drivers of user behavior, the culture of social comparison, and the pervasiveness of social network in everyday life collectively make FoMO the most pronouncedly correlated anxiety type with PSNU.

Additionally, we conducted subgroup analyses on the timing of measurement (before COVID-19 vs. during COVID-19), measurement tools (for PSNU and anxiety symptoms), sample characteristics (participants’ region), and performed a meta-regression analysis on gender and age in the context of PSNU and anxiety symptoms. It was found that the timing of measurement, tools used for assessing PSNU and anxiety, region, and gender had a moderating effect, whereas age did not show a significant moderating impact.

Firstly, the relationship between PSNU and anxiety symptoms was significantly higher during the COVID-19 period than before, especially between PSNU and GA. However, the moderating effect of measurement timing was not significant in the relationship between PSNU and other types of anxiety. This could be attributed to the increased uncertainty and stress during the pandemic, leading to heightened levels of general anxiety [ 227 ]. The overuse of social network for information seeking and anxiety alleviation might have paradoxically exacerbated anxiety symptoms, particularly among individuals with broad future-related worries [ 228 ]. While the COVID-19 pandemic altered the relationship between PSNU and GA, its impact on other types of anxiety (such as SA and AA) may not have been significant, likely due to these anxiety types being more influenced by other factors like social skills and attachment styles, which were minimally impacted by the epidemic.

Secondly, the observed variance in the relationship between PSNU and AA across different economic contexts, notably between developing and developed countries, underscores the multifaceted influence of socio-economic, cultural, and technological factors on this dynamic. The amplified connection in developing countries may be attributed to greater socio-economic challenges, distinct cultural norms regarding social support and interaction, rising social network penetration, especially among younger demographics, and technological disparities influencing accessibility and user experience [ 229 , 230 ]. Moreover, the role of social network as a coping mechanism for emotional distress, potentially fostering insecure attachment patterns, is more pronounced in these settings [ 231 ]. These findings highlight the necessity of considering contextual variations in assessing the psychological impacts of social network, advocating for a nuanced understanding of how socio-economic and cultural backgrounds mediate the relationship between PSNU and mental health outcomes [ 232 ]. Additionally, the relationship between PSNU and other types of anxiety (such as GA and SA) presents uniform characteristics across different economic contexts.

Thirdly, the significant moderating effects of measurement tools in the context of PSNU and its correlation with various forms of anxiety, including GA, and AA, are crucial in interpreting the research findings. Specifically, the study reveals that the Bergen Social Media Addiction Scale (BSMAS) demonstrates a stronger correlation between PSNU and GA, compared to other tools. Similarly, for AA, the Griffiths’ Problematic Internet Use Scale 2 (GPIUS2) and the Problematic Media Social Media Use Assessment Questionnaire (PMSMUAQ) show a more pronounced correlation with AA than the BSMAS or other instruments, but for SA and FoMO, the PSNU instrument doesn’t significantly moderate the correlation. The PSNU measurement tool typically contains an emotional change dimension. SA and FoMO, due to their specific conditional stimuli triggers and correlation with social networks [ 233 , 234 ], are likely to yield more consistent scores in this dimension, while GA and AA may be less reliable due to their lesser sensitivity to specific conditional stimuli. Consequently, the adjustment effects of PSNU measurements vary across anxiety symptoms. Regarding the measurement tools for anxiety, different scales exhibit varying degrees of sensitivity in detecting the relationship with PSNU. The Generalized Anxiety Disorder Scale (GAD) and the Depression Anxiety Stress Scales 21 (DASS-21) are more effective in illustrating a strong relationship between GA and PSNU than the State-Trait Anxiety Inventory (STAI). In the case of AA, the Experiences in Close Relationships-21 (ECR-21) provides a more substantial correlation than the Relationship Questionnaire (RQ). Furthermore, for FoMO, the Fear of Missing Out Scale - Multi-Social Media Environment (FoMOS-MSME) is more indicative of a strong relationship with PSNU compared to the standard FoMOS or the T-S FoMOS. These findings underscore the importance of the selection of appropriate measurement tools in research. Different tools, due to their unique design, focus, and sensitivity, can reveal varying degrees of correlation between PSNU and anxiety disorders. This highlights the need for careful consideration of tool characteristics and their potential impact on research outcomes. It also cautions against drawing direct comparisons between studies without acknowledging the possible variances introduced by the use of different measurement instruments.

Fourthly, the significant moderating role of gender in the relationship between PSNU and SA, particularly pronounced in samples with a higher proportion of females. Women tend to engage more actively and emotionally with social network, potentially leading to an increased dependency on these platforms when confronting social anxiety [ 235 ]. This intensified use might amplify the association between PSNU and SA. Societal and cultural pressures, especially those related to appearance and social status, are known to disproportionately affect women, possibly exacerbating their experience of social anxiety and prompting a greater reliance on social network for validation and support [ 236 ]. Furthermore, women’s propensity to seek emotional support and express themselves on social network platforms [ 237 ] could strengthen this link, particularly in the context of managing social anxiety. Consequently, the observed gender differences in the relationship between PSNU and SA underscore the importance of considering gender-specific dynamics and cultural influences in psychological research related to social network use. In addition, gender consistency was observed in the association between PSNU and other types of anxiety, indicating no significant gender disparities.

Fifthly, the absence of a significant moderating effect of age on the relationship between PSNU and various forms of anxiety suggests a pervasive influence of social network across different age groups. This finding indicates that the impact of PSNU on anxiety is relatively consistent, irrespective of age, highlighting the universal nature of social network’s psychological implications [ 238 ]. Furthermore, this uniformity suggests that other factors, such as individual psychological traits or socio-cultural influences, might play a more crucial role in the development of anxiety related to social networking usage than age [ 239 ]. The non-significant role of age also points towards a potential generational overlap in social networking usage patterns and their psychological effects, challenging the notion that younger individuals are uniquely susceptible to the adverse effects of social network on mental health [ 240 ]. Therefore, this insight necessitates a broader perspective in understanding the dynamics of social network and mental health, one that transcends age-based assumptions.

Limitations

There are some limitations in this research. First, most of the studies were cross-sectional surveys, resulting in difficulties in inferring causality of variables, longitudinal study data will be needed to evaluate causal interactions in the future. Second, considerable heterogeneity was found in the estimated results, although heterogeneity can be partially explained by differences in study design (e.g., Time of measurement, region, gender, and measurement tools), but this can introduce some uncertainty in the aggregation and generalization of the estimated results. Third, most studies were based on Asian samples, which limits the generality of the results. Fourth, to minimize potential sources of heterogeneity, some less frequently used measurement tools were not included in the classification of measurement tools, which may have some impact on the results of heterogeneity interpretation. Finally, since most of the included studies used self-reported scales, it is possible to get results that deviate from the actual situation to some extent.

This meta-analysis aims to quantifies the correlations between PSNU and four specific types of anxiety symptoms (i.e., generalized anxiety, social anxiety, attachment anxiety, and fear of missing out). The results revealed a significant moderate positive association between PSNU and each of these anxiety symptoms. Furthermore, Subgroup analysis and meta-regression analysis indicated that gender, region, time of measurement, and instrument of measurement significantly influenced the relationship between PSNU and specific anxiety symptoms. Specifically, the measurement time and GA measurement tools significantly influenced the relationship between PSNU and GA. Gender significantly influenced the relationship between PSNU and SA. Region, PSNU measurement tools, and AA measurement tools all significantly influenced the relationship between PSNU and AA. The FoMO measurement tool significantly influenced the relationship between PSNU and FoMO. Regarding these findings, prevention interventions for PSNU and anxiety symptoms are important.

Data availability

The datasets are available from the corresponding author on reasonable request.

Abbreviations

• Problematic social networking use
• Generalized anxiety
• Social anxiety
• Attachment anxiety

Fear of miss out

Bergen Social Media Addiction Scale

Facebook Addiction Scale

Facebook Intrusion Questionnaire

Generalized Problematic Internet Use Scale 2

Problematic Mobile Social Media Usage Assessment Questionnaire

Social Network Addiction Tendency Scale

Brief Symptom Inventory

The anxiety subscale of the Depression Anxiety Stress Scales

Generalized Anxiety Disorder

The anxiety subscale of the Hospital Anxiety and Depression Scale

State-Trait Anxiety Inventory

Interaction Anxiousness Scale

Liebowitz Social Anxiety Scale

Social Anxiety Scale for Social Media Users

Social Anxiety for Adolescents

Social Anxiety Subscale of the Self-Consciousness Scale

Social Interaction Anxiety Scale

Experiences in Close Relationship Scale

Relationship questionnaire

Fear of Missing Out Scale

FoMO Measurement Scale in the Mobile Social Media Environment

Trait-State Fear of missing Out Scale

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This research was supported by the Social Science Foundation of China (Grant Number: 23BSH135).

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Du, M., Zhao, C., Hu, H. et al. Association between problematic social networking use and anxiety symptoms: a systematic review and meta-analysis. BMC Psychol 12 , 263 (2024). https://doi.org/10.1186/s40359-024-01705-w

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Is the Internet bad for you? Huge study reveals surprise effect on well-being

• Carissa Wong

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People who had access to the Internet scored higher on measures of life satisfaction in a global survey. Credit: Ute Grabowsky/Photothek via Getty

A global, 16-year study 1 of 2.4 million people has found that Internet use might boost measures of well-being, such as life satisfaction and sense of purpose — challenging the commonly held idea that Internet use has negative effects on people’s welfare.

US TikTok ban: how the looming restriction is affecting scientists on the app

“It’s an important piece of the puzzle on digital-media use and mental health,” says psychologist Markus Appel at the University of Würzburg in Germany. “If social media and Internet and mobile-phone use is really such a devastating force in our society, we should see it on this bird’s-eye view [study] — but we don’t.” Such concerns are typically related to behaviours linked to social-media use, such as cyberbullying, social-media addiction and body-image issues. But the best studies have so far shown small negative effects, if any 2 , 3 , of Internet use on well-being, says Appel.

The authors of the latest study, published on 13 May in Technology, Mind and Behaviour , sought to capture a more global picture of the Internet’s effects than did previous research. “While the Internet is global, the study of it is not,” said Andrew Przybylski, a researcher at the University of Oxford, UK, who studies how technology affects well-being, in a press briefing on 9 May. “More than 90% of data sets come from a handful of English-speaking countries” that are mostly in the global north, he said. Previous studies have also focused on young people, he added.

To address this research gap, Pryzbylski and his colleagues analysed data on how Internet access was related to eight measures of well-being from the Gallup World Poll , conducted by analytics company Gallup, based in Washington DC. The data were collected annually from 2006 to 2021 from 1,000 people, aged 15 and above, in 168 countries, through phone or in-person interviews. The researchers controlled for factors that might affect Internet use and welfare, including income level, employment status, education level and health problems.

Like a walk in nature

The team found that, on average, people who had access to the Internet scored 8% higher on measures of life satisfaction, positive experiences and contentment with their social life, compared with people who lacked web access. Online activities can help people to learn new things and make friends, and this could contribute to the beneficial effects, suggests Appel.

The positive effect is similar to the well-being benefit associated with taking a walk in nature, says Przybylski.

However, women aged 15–24 who reported having used the Internet in the past week were, on average, less happy with the place they live, compared with people who didn’t use the web. This could be because people who do not feel welcome in their community spend more time online, said Przybylski. Further studies are needed to determine whether links between Internet use and well-being are causal or merely associations, he added.

The study comes at a time of discussion around the regulation of Internet and social-media use , especially among young people. “The study cannot contribute to the recent debate on whether or not social-media use is harmful, or whether or not smartphones should be banned at schools,” because the study was not designed to answer these questions, says Tobias Dienlin, who studies how social media affects well-being at the University of Vienna. “Different channels and uses of the Internet have vastly different effects on well-being outcomes,” he says.

doi: https://doi.org/10.1038/d41586-024-01410-z

Vuorre, M. & Przybylski, A. K. Technol. Mind Behav . https://doi.org/10.1037/tmb0000127 (2024).

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Heffer, T. et al. Clin. Psychol. Sci. 7 , 462–470 (2018).

Coyne, S. M., Rogers, A. A., Zurcher, J. D., Stockdale, L. & Booth, M. Comput. Hum. Behav . 104 , 106160 (2020).

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Social media use can be positive for mental health and well-being

January 6, 2020— Mesfin Awoke Bekalu , research scientist in the Lee Kum Sheung Center for Health and Happiness at Harvard T.H. Chan School of Public Health, discusses a new study he co-authored on associations between social media use and mental health and well-being.

What is healthy vs. potentially problematic social media use?

Our study has brought preliminary evidence to answer this question. Using a nationally representative sample, we assessed the association of two dimensions of social media use—how much it’s routinely used and how emotionally connected users are to the platforms—with three health-related outcomes: social well-being, positive mental health, and self-rated health.

We found that routine social media use—for example, using social media as part of everyday routine and responding to content that others share—is positively associated with all three health outcomes. Emotional connection to social media—for example, checking apps excessively out of fear of missing out, being disappointed about or feeling disconnected from friends when not logged into social media—is negatively associated with all three outcomes.

In more general terms, these findings suggest that as long as we are mindful users, routine use may not in itself be a problem. Indeed, it could be beneficial.

For those with unhealthy social media use, behavioral interventions may help. For example, programs that develop “effortful control” skills—the ability to self-regulate behavior—have been widely shown to be useful in dealing with problematic Internet and social media use.

We’re used to hearing that social media use is harmful to mental health and well-being, particularly for young people. Did it surprise you to find that it can have positive effects?

The findings go against what some might expect, which is intriguing. We know that having a strong social network is associated with positive mental health and well-being. Routine social media use may compensate for diminishing face-to-face social interactions in people’s busy lives. Social media may provide individuals with a platform that overcomes barriers of distance and time, allowing them to connect and reconnect with others and thereby expand and strengthen their in-person networks and interactions. Indeed, there is some empirical evidence supporting this.

On the other hand, a growing body of research has demonstrated that social media use is negatively associated with mental health and well-being, particularly among young people—for example, it may contribute to increased risk of depression and anxiety symptoms.

Our findings suggest that the ways that people are using social media may have more of an impact on their mental health and well-being than just the frequency and duration of their use.

What disparities did you find in the ways that social media use benefits and harms certain populations? What concerns does this raise?

My co-authors Rachel McCloud , Vish Viswanath , and I found that the benefits and harms associated with social media use varied across demographic, socioeconomic, and racial population sub-groups. Specifically, while the benefits were generally associated with younger age, better education, and being white, the harms were associated with older age, less education, and being a racial minority. Indeed, these findings are consistent with the body of work on communication inequalities and health disparities that our lab, the Viswanath lab , has documented over the past 15 or so years. We know that education, income, race, and ethnicity influence people’s access to, and ability to act on, health information from media, including the Internet. The concern is that social media may perpetuate those differences.

— Amy Roeder

Reviewed by Psychology Today Staff

Empathy is the ability to recognize, understand, and share the thoughts and feelings of another person, animal, or fictional character. Developing empathy is crucial for establishing relationships and behaving compassionately. It involves experiencing another person’s point of view, rather than just one’s own, and enables prosocial or helping behaviors that come from within, rather than being forced.

Some surveys indicate that empathy is on the decline in the United States and elsewhere, findings that motivate parents, schools, and communities to support programs that help people of all ages enhance and maintain their ability to walk in each other’s shoes.

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Empathy helps us cooperate with others, build friendships, make moral decisions, and intervene when we see others being bullied. Humans begin to show signs of empathy in infancy and the trait develops steadily through childhood and adolescence . Still, most people are likely to feel greater empathy for people like themselves and may feel less empathy for those outside their family, community, ethnicity , or race.

Empathy helps us connect and help others, but like other traits, it may have evolved with a selfish motive: using others as a “social antenna” to help detect danger. From an evolutionary perspective, creating a mental model of another person's intent is critical: the arrival of an interloper, for example, could be deadly, so developing sensitivity to the signals of others could be life-saving.

Babies display an understanding that people’s actions are guided by intentions and are able to act on that understanding before they are 18 months old , including trying to comfort a parent. More advanced reasoning about other people’s thoughts develops by around age 5 or 6, and research shows that parents who promote and model empathy raise more empathetic children.

Empathy, sympathy, and compassion are often used interchangeably, but they are not the same . Sympathy is feeling of concern for someone else, and a desire that they become happier or better off, while empathy involves sharing the other person’s emotions. Compassion is an empathic understanding of a person's feelings accompanied by altruism , or a desire to act on that person's behalf. 

Researchers believe people can choose to cultivate and prioritize empathy. People who spend more time with individuals different from themselves tend to adopt a more empathic outlook toward others. Other research finds that reading novels can help foster the ability to put ourselves in the minds of others. Meditation has also been shown to help cultivate brain states that increase empathy.

Some neuroscientists have advanced the concept of "mirror neurons” as a possible source of empathy . These neurons, it is theorized, enhance the capacity to display, read, and mimic emotional signals through facial expressions and other forms of  body language , enhancing empathy. But whether mirror neurons actually operate this way in humans is a subject of longstanding scientific debate, and some scientists question their very existence. 

The ability to convey support for a partner, relative, or friend is crucial to establishing positive relationships. Empathy enables us to establish rapport with another person , make them feel that they are being heard, and, through words and body language, mimic their emotions. Perspective-taking , or the empathic ability to assume the cognitive state of another person and see a problem through their eyes, can further cement a connection.

In healthy relationships, people expect their partners to empathize with them when they face hardship or personal struggles, but the ability to empathize with a partner in good times may be at least as important. In one study, displaying empathy for a partner’s positive emotions was five times more beneficial for relationship satisfaction than only empathizing with his or her negative emotions.

People high in narcissism, or who have narcissistic personality disorder , can exhibit empathy and even compassion. However, that ability only goes so far, as ultimately their own needs come first. Some researchers believe narcissists can develop greater empathy by developing greater self-compassion, which can increase their own feelings of security and self-worth and enable them to open up to hearing others.

Putting yourself in someone else’s shoes can be beneficial, but when it becomes one’s default mode of relating to others, it can blind an individual to their own needs and even make them vulnerable to those who would take advantage of them.

People who regularly put the feelings and perspectives of others above their own may experience feelings of emptiness or alienation and develop generalized anxiety or low-level depression . Psychopaths, on the other hand, are capable of empathic accuracy , or correctly inferring thoughts and feelings, but may have no experiential referent for it: a true psychopath does not feel empathy.

First responders, humanitarian aid workers, doctors, therapists, journalists, and others whose work involves opening themselves up to others’ pain tend to be highly empathic. However, they may come to share the heartbreak of those they help or whose stories they record. As such “emotional residue” accumulates, they may shut down , burn out , and become less willing or able to give of themselves.

Empaths are often characterized as being highly sensitive and overly focused on the needs of others. They may benefit from time alone, as they find it draining to be in the presence of other people. People who are very empathic are more likely to be targeted by manipulative individuals. For this reason, it is important to create healthy boundaries in all relationships, and to be cognizant of relationships with "energy vampires," who are draining to empaths and non-empaths alike.

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The effect of social media on the development of students’ affective variables

1 Science and Technology Department, Nanjing University of Posts and Telecommunications, Nanjing, China

2 School of Marxism, Hohai University, Nanjing, Jiangsu, China

3 Government Enterprise Customer Center, China Mobile Group Jiangsu Co., Ltd., Nanjing, China

The use of social media is incomparably on the rise among students, influenced by the globalized forms of communication and the post-pandemic rush to use multiple social media platforms for education in different fields of study. Though social media has created tremendous chances for sharing ideas and emotions, the kind of social support it provides might fail to meet students’ emotional needs, or the alleged positive effects might be short-lasting. In recent years, several studies have been conducted to explore the potential effects of social media on students’ affective traits, such as stress, anxiety, depression, and so on. The present paper reviews the findings of the exemplary published works of research to shed light on the positive and negative potential effects of the massive use of social media on students’ emotional well-being. This review can be insightful for teachers who tend to take the potential psychological effects of social media for granted. They may want to know more about the actual effects of the over-reliance on and the excessive (and actually obsessive) use of social media on students’ developing certain images of self and certain emotions which are not necessarily positive. There will be implications for pre- and in-service teacher training and professional development programs and all those involved in student affairs.

Introduction

Social media has turned into an essential element of individuals’ lives including students in today’s world of communication. Its use is growing significantly more than ever before especially in the post-pandemic era, marked by a great revolution happening to the educational systems. Recent investigations of using social media show that approximately 3 billion individuals worldwide are now communicating via social media ( Iwamoto and Chun, 2020 ). This growing population of social media users is spending more and more time on social network groupings, as facts and figures show that individuals spend 2 h a day, on average, on a variety of social media applications, exchanging pictures and messages, updating status, tweeting, favoring, and commenting on many updated socially shared information ( Abbott, 2017 ).

Researchers have begun to investigate the psychological effects of using social media on students’ lives. Chukwuere and Chukwuere (2017) maintained that social media platforms can be considered the most important source of changing individuals’ mood, because when someone is passively using a social media platform seemingly with no special purpose, s/he can finally feel that his/her mood has changed as a function of the nature of content overviewed. Therefore, positive and negative moods can easily be transferred among the population using social media networks ( Chukwuere and Chukwuere, 2017 ). This may become increasingly important as students are seen to be using social media platforms more than before and social networking is becoming an integral aspect of their lives. As described by Iwamoto and Chun (2020) , when students are affected by social media posts, especially due to the increasing reliance on social media use in life, they may be encouraged to begin comparing themselves to others or develop great unrealistic expectations of themselves or others, which can have several affective consequences.

Considering the increasing influence of social media on education, the present paper aims to focus on the affective variables such as depression, stress, and anxiety, and how social media can possibly increase or decrease these emotions in student life. The exemplary works of research on this topic in recent years will be reviewed here, hoping to shed light on the positive and negative effects of these ever-growing influential platforms on the psychology of students.

Significance of the study

Though social media, as the name suggests, is expected to keep people connected, probably this social connection is only superficial, and not adequately deep and meaningful to help individuals feel emotionally attached to others. The psychological effects of social media on student life need to be studied in more depth to see whether social media really acts as a social support for students and whether students can use social media to cope with negative emotions and develop positive feelings or not. In other words, knowledge of the potential effects of the growing use of social media on students’ emotional well-being can bridge the gap between the alleged promises of social media and what it actually has to offer to students in terms of self-concept, self-respect, social role, and coping strategies (for stress, anxiety, etc.).

Exemplary general literature on psychological effects of social media

Before getting down to the effects of social media on students’ emotional well-being, some exemplary works of research in recent years on the topic among general populations are reviewed. For one, Aalbers et al. (2018) reported that individuals who spent more time passively working with social media suffered from more intense levels of hopelessness, loneliness, depression, and perceived inferiority. For another, Tang et al. (2013) observed that the procedures of sharing information, commenting, showing likes and dislikes, posting messages, and doing other common activities on social media are correlated with higher stress. Similarly, Ley et al. (2014) described that people who spend 2 h, on average, on social media applications will face many tragic news, posts, and stories which can raise the total intensity of their stress. This stress-provoking effect of social media has been also pinpointed by Weng and Menczer (2015) , who contended that social media becomes a main source of stress because people often share all kinds of posts, comments, and stories ranging from politics and economics, to personal and social affairs. According to Iwamoto and Chun (2020) , anxiety and depression are the negative emotions that an individual may develop when some source of stress is present. In other words, when social media sources become stress-inducing, there are high chances that anxiety and depression also develop.

Charoensukmongkol (2018) reckoned that the mental health and well-being of the global population can be at a great risk through the uncontrolled massive use of social media. These researchers also showed that social media sources can exert negative affective impacts on teenagers, as they can induce more envy and social comparison. According to Fleck and Johnson-Migalski (2015) , though social media, at first, plays the role of a stress-coping strategy, when individuals continue to see stressful conditions (probably experienced and shared by others in media), they begin to develop stress through the passage of time. Chukwuere and Chukwuere (2017) maintained that social media platforms continue to be the major source of changing mood among general populations. For example, someone might be passively using a social media sphere, and s/he may finally find him/herself with a changed mood depending on the nature of the content faced. Then, this good or bad mood is easily shared with others in a flash through the social media. Finally, as Alahmar (2016) described, social media exposes people especially the young generation to new exciting activities and events that may attract them and keep them engaged in different media contexts for hours just passing their time. It usually leads to reduced productivity, reduced academic achievement, and addiction to constant media use ( Alahmar, 2016 ).

The number of studies on the potential psychological effects of social media on people in general is higher than those selectively addressed here. For further insights into this issue, some other suggested works of research include Chang (2012) , Sriwilai and Charoensukmongkol (2016) , and Zareen et al. (2016) . Now, we move to the studies that more specifically explored the effects of social media on students’ affective states.

Review of the affective influences of social media on students

Vygotsky’s mediational theory (see Fernyhough, 2008 ) can be regarded as a main theoretical background for the support of social media on learners’ affective states. Based on this theory, social media can play the role of a mediational means between learners and the real environment. Learners’ understanding of this environment can be mediated by the image shaped via social media. This image can be either close to or different from the reality. In the case of the former, learners can develop their self-image and self-esteem. In the case of the latter, learners might develop unrealistic expectations of themselves by comparing themselves to others. As it will be reviewed below among the affective variables increased or decreased in students under the influence of the massive use of social media are anxiety, stress, depression, distress, rumination, and self-esteem. These effects have been explored more among school students in the age range of 13–18 than university students (above 18), but some studies were investigated among college students as well. Exemplary works of research on these affective variables are reviewed here.

In a cross-sectional study, O’Dea and Campbell (2011) explored the impact of online interactions of social networks on the psychological distress of adolescent students. These researchers found a negative correlation between the time spent on social networking and mental distress. Dumitrache et al. (2012) explored the relations between depression and the identity associated with the use of the popular social media, the Facebook. This study showed significant associations between depression and the number of identity-related information pieces shared on this social network. Neira and Barber (2014) explored the relationship between students’ social media use and depressed mood at teenage. No significant correlation was found between these two variables. In the same year, Tsitsika et al. (2014) explored the associations between excessive use of social media and internalizing emotions. These researchers found a positive correlation between more than 2-h a day use of social media and anxiety and depression.

Hanprathet et al. (2015) reported a statistically significant positive correlation between addiction to Facebook and depression among about a thousand high school students in wealthy populations of Thailand and warned against this psychological threat. Sampasa-Kanyinga and Lewis (2015) examined the relationship between social media use and psychological distress. These researchers found that the use of social media for more than 2 h a day was correlated with a higher intensity of psychological distress. Banjanin et al. (2015) tested the relationship between too much use of social networking and depression, yet found no statistically significant correlation between these two variables. Frison and Eggermont (2016) examined the relationships between different forms of Facebook use, perceived social support of social media, and male and female students’ depressed mood. These researchers found a positive association between the passive use of the Facebook and depression and also between the active use of the social media and depression. Furthermore, the perceived social support of the social media was found to mediate this association. Besides, gender was found as the other factor to mediate this relationship.

Vernon et al. (2017) explored change in negative investment in social networking in relation to change in depression and externalizing behavior. These researchers found that increased investment in social media predicted higher depression in adolescent students, which was a function of the effect of higher levels of disrupted sleep. Barry et al. (2017) explored the associations between the use of social media by adolescents and their psychosocial adjustment. Social media activity showed to be positively and moderately associated with depression and anxiety. Another investigation was focused on secondary school students in China conducted by Li et al. (2017) . The findings showed a mediating role of insomnia on the significant correlation between depression and addiction to social media. In the same year, Yan et al. (2017) aimed to explore the time spent on social networks and its correlation with anxiety among middle school students. They found a significant positive correlation between more than 2-h use of social networks and the intensity of anxiety.

Also in China, Wang et al. (2018) showed that addiction to social networking sites was correlated positively with depression, and this correlation was mediated by rumination. These researchers also found that this mediating effect was moderated by self-esteem. It means that the effect of addiction on depression was compounded by low self-esteem through rumination. In another work of research, Drouin et al. (2018) showed that though social media is expected to act as a form of social support for the majority of university students, it can adversely affect students’ mental well-being, especially for those who already have high levels of anxiety and depression. In their research, the social media resources were found to be stress-inducing for half of the participants, all university students. The higher education population was also studied by Iwamoto and Chun (2020) . These researchers investigated the emotional effects of social media in higher education and found that the socially supportive role of social media was overshadowed in the long run in university students’ lives and, instead, fed into their perceived depression, anxiety, and stress.

Keles et al. (2020) provided a systematic review of the effect of social media on young and teenage students’ depression, psychological distress, and anxiety. They found that depression acted as the most frequent affective variable measured. The most salient risk factors of psychological distress, anxiety, and depression based on the systematic review were activities such as repeated checking for messages, personal investment, the time spent on social media, and problematic or addictive use. Similarly, Mathewson (2020) investigated the effect of using social media on college students’ mental health. The participants stated the experience of anxiety, depression, and suicidality (thoughts of suicide or attempts to suicide). The findings showed that the types and frequency of using social media and the students’ perceived mental health were significantly correlated with each other.

The body of research on the effect of social media on students’ affective and emotional states has led to mixed results. The existing literature shows that there are some positive and some negative affective impacts. Yet, it seems that the latter is pre-dominant. Mathewson (2020) attributed these divergent positive and negative effects to the different theoretical frameworks adopted in different studies and also the different contexts (different countries with whole different educational systems). According to Fredrickson’s broaden-and-build theory of positive emotions ( Fredrickson, 2001 ), the mental repertoires of learners can be built and broadened by how they feel. For instance, some external stimuli might provoke negative emotions such as anxiety and depression in learners. Having experienced these negative emotions, students might repeatedly check their messages on social media or get addicted to them. As a result, their cognitive repertoire and mental capacity might become limited and they might lose their concentration during their learning process. On the other hand, it should be noted that by feeling positive, learners might take full advantage of the affordances of the social media and; thus, be able to follow their learning goals strategically. This point should be highlighted that the link between the use of social media and affective states is bi-directional. Therefore, strategic use of social media or its addictive use by students can direct them toward either positive experiences like enjoyment or negative ones such as anxiety and depression. Also, these mixed positive and negative effects are similar to the findings of several other relevant studies on general populations’ psychological and emotional health. A number of studies (with general research populations not necessarily students) showed that social networks have facilitated the way of staying in touch with family and friends living far away as well as an increased social support ( Zhang, 2017 ). Given the positive and negative emotional effects of social media, social media can either scaffold the emotional repertoire of students, which can develop positive emotions in learners, or induce negative provokers in them, based on which learners might feel negative emotions such as anxiety and depression. However, admittedly, social media has also generated a domain that encourages the act of comparing lives, and striving for approval; therefore, it establishes and internalizes unrealistic perceptions ( Virden et al., 2014 ; Radovic et al., 2017 ).

It should be mentioned that the susceptibility of affective variables to social media should be interpreted from a dynamic lens. This means that the ecology of the social media can make changes in the emotional experiences of learners. More specifically, students’ affective variables might self-organize into different states under the influence of social media. As for the positive correlation found in many studies between the use of social media and such negative effects as anxiety, depression, and stress, it can be hypothesized that this correlation is induced by the continuous comparison the individual makes and the perception that others are doing better than him/her influenced by the posts that appear on social media. Using social media can play a major role in university students’ psychological well-being than expected. Though most of these studies were correlational, and correlation is not the same as causation, as the studies show that the number of participants experiencing these negative emotions under the influence of social media is significantly high, more extensive research is highly suggested to explore causal effects ( Mathewson, 2020 ).

As the review of exemplary studies showed, some believed that social media increased comparisons that students made between themselves and others. This finding ratifies the relevance of the Interpretation Comparison Model ( Stapel and Koomen, 2000 ; Stapel, 2007 ) and Festinger’s (1954) Social Comparison Theory. Concerning the negative effects of social media on students’ psychology, it can be argued that individuals may fail to understand that the content presented in social media is usually changed to only represent the attractive aspects of people’s lives, showing an unrealistic image of things. We can add that this argument also supports the relevance of the Social Comparison Theory and the Interpretation Comparison Model ( Stapel and Koomen, 2000 ; Stapel, 2007 ), because social media sets standards that students think they should compare themselves with. A constant observation of how other students or peers are showing their instances of achievement leads to higher self-evaluation ( Stapel and Koomen, 2000 ). It is conjectured that the ubiquitous role of social media in student life establishes unrealistic expectations and promotes continuous comparison as also pinpointed in the Interpretation Comparison Model ( Stapel and Koomen, 2000 ; Stapel, 2007 ).

Implications of the study

The use of social media is ever increasing among students, both at school and university, which is partly because of the promises of technological advances in communication services and partly because of the increased use of social networks for educational purposes in recent years after the pandemic. This consistent use of social media is not expected to leave students’ psychological, affective and emotional states untouched. Thus, it is necessary to know how the growing usage of social networks is associated with students’ affective health on different aspects. Therefore, we found it useful to summarize the research findings in recent years in this respect. If those somehow in charge of student affairs in educational settings are aware of the potential positive or negative effects of social media usage on students, they can better understand the complexities of students’ needs and are better capable of meeting them.

Psychological counseling programs can be initiated at schools or universities to check upon the latest state of students’ mental and emotional health influenced by the pervasive use of social media. The counselors can be made aware of the potential adverse effects of social networking and can adapt the content of their inquiries accordingly. Knowledge of the potential reasons for student anxiety, depression, and stress can help school or university counselors to find individualized coping strategies when they diagnose any symptom of distress in students influenced by an excessive use of social networking.

Admittedly, it is neither possible to discard the use of social media in today’s academic life, nor to keep students’ use of social networks fully controlled. Certainly, the educational space in today’s world cannot do without the social media, which has turned into an integral part of everybody’s life. Yet, probably students need to be instructed on how to take advantage of the media and to be the least affected negatively by its occasional superficial and unrepresentative content. Compensatory programs might be needed at schools or universities to encourage students to avoid making unrealistic and impartial comparisons of themselves and the flamboyant images of others displayed on social media. Students can be taught to develop self-appreciation and self-care while continuing to use the media to their benefit.

The teachers’ role as well as the curriculum developers’ role are becoming more important than ever, as they can significantly help to moderate the adverse effects of the pervasive social media use on students’ mental and emotional health. The kind of groupings formed for instructional purposes, for example, in social media can be done with greater care by teachers to make sure that the members of the groups are homogeneous and the tasks and activities shared in the groups are quite relevant and realistic. The teachers cannot always be in a full control of students’ use of social media, and the other fact is that students do not always and only use social media for educational purposes. They spend more time on social media for communicating with friends or strangers or possibly they just passively receive the content produced out of any educational scope just for entertainment. This uncontrolled and unrealistic content may give them a false image of life events and can threaten their mental and emotional health. Thus, teachers can try to make students aware of the potential hazards of investing too much of their time on following pages or people that publish false and misleading information about their personal or social identities. As students, logically expected, spend more time with their teachers than counselors, they may be better and more receptive to the advice given by the former than the latter.

Teachers may not be in full control of their students’ use of social media, but they have always played an active role in motivating or demotivating students to take particular measures in their academic lives. If teachers are informed of the recent research findings about the potential effects of massively using social media on students, they may find ways to reduce students’ distraction or confusion in class due to the excessive or over-reliant use of these networks. Educators may more often be mesmerized by the promises of technology-, computer- and mobile-assisted learning. They may tend to encourage the use of social media hoping to benefit students’ social and interpersonal skills, self-confidence, stress-managing and the like. Yet, they may be unaware of the potential adverse effects on students’ emotional well-being and, thus, may find the review of the recent relevant research findings insightful. Also, teachers can mediate between learners and social media to manipulate the time learners spend on social media. Research has mainly indicated that students’ emotional experiences are mainly dependent on teachers’ pedagogical approach. They should refrain learners from excessive use of, or overreliance on, social media. Raising learners’ awareness of this fact that individuals should develop their own path of development for learning, and not build their development based on unrealistic comparison of their competences with those of others, can help them consider positive values for their activities on social media and, thus, experience positive emotions.

At higher education, students’ needs are more life-like. For example, their employment-seeking spirits might lead them to create accounts in many social networks, hoping for a better future. However, membership in many of these networks may end in the mere waste of the time that could otherwise be spent on actual on-campus cooperative projects. Universities can provide more on-campus resources both for research and work experience purposes from which the students can benefit more than the cyberspace that can be tricky on many occasions. Two main theories underlying some negative emotions like boredom and anxiety are over-stimulation and under-stimulation. Thus, what learners feel out of their involvement in social media might be directed toward negative emotions due to the stimulating environment of social media. This stimulating environment makes learners rely too much, and spend too much time, on social media or use them obsessively. As a result, they might feel anxious or depressed. Given the ubiquity of social media, these negative emotions can be replaced with positive emotions if learners become aware of the psychological effects of social media. Regarding the affordances of social media for learners, they can take advantage of the potential affordances of these media such as improving their literacy, broadening their communication skills, or enhancing their distance learning opportunities.

A review of the research findings on the relationship between social media and students’ affective traits revealed both positive and negative findings. Yet, the instances of the latter were more salient and the negative psychological symptoms such as depression, anxiety, and stress have been far from negligible. These findings were discussed in relation to some more relevant theories such as the social comparison theory, which predicted that most of the potential issues with the young generation’s excessive use of social media were induced by the unfair comparisons they made between their own lives and the unrealistic portrayal of others’ on social media. Teachers, education policymakers, curriculum developers, and all those in charge of the student affairs at schools and universities should be made aware of the psychological effects of the pervasive use of social media on students, and the potential threats.

It should be reminded that the alleged socially supportive and communicative promises of the prevalent use of social networking in student life might not be fully realized in practice. Students may lose self-appreciation and gratitude when they compare their current state of life with the snapshots of others’ or peers’. A depressed or stressed-out mood can follow. Students at schools or universities need to learn self-worth to resist the adverse effects of the superficial support they receive from social media. Along this way, they should be assisted by the family and those in charge at schools or universities, most importantly the teachers. As already suggested, counseling programs might help with raising students’ awareness of the potential psychological threats of social media to their health. Considering the ubiquity of social media in everybody’ life including student life worldwide, it seems that more coping and compensatory strategies should be contrived to moderate the adverse psychological effects of the pervasive use of social media on students. Also, the affective influences of social media should not be generalized but they need to be interpreted from an ecological or contextual perspective. This means that learners might have different emotions at different times or different contexts while being involved in social media. More specifically, given the stative approach to learners’ emotions, what learners emotionally experience in their application of social media can be bound to their intra-personal and interpersonal experiences. This means that the same learner at different time points might go through different emotions Also, learners’ emotional states as a result of their engagement in social media cannot be necessarily generalized to all learners in a class.

As the majority of studies on the psychological effects of social media on student life have been conducted on school students than in higher education, it seems it is too soon to make any conclusive remark on this population exclusively. Probably, in future, further studies of the psychological complexities of students at higher education and a better knowledge of their needs can pave the way for making more insightful conclusions about the effects of social media on their affective states.

Suggestions for further research

The majority of studies on the potential effects of social media usage on students’ psychological well-being are either quantitative or qualitative in type, each with many limitations. Presumably, mixed approaches in near future can better provide a comprehensive assessment of these potential associations. Moreover, most studies on this topic have been cross-sectional in type. There is a significant dearth of longitudinal investigation on the effect of social media on developing positive or negative emotions in students. This seems to be essential as different affective factors such as anxiety, stress, self-esteem, and the like have a developmental nature. Traditional research methods with single-shot designs for data collection fail to capture the nuances of changes in these affective variables. It can be expected that more longitudinal studies in future can show how the continuous use of social media can affect the fluctuations of any of these affective variables during the different academic courses students pass at school or university.

As already raised in some works of research reviewed, the different patterns of impacts of social media on student life depend largely on the educational context. Thus, the same research designs with the same academic grade students and even the same age groups can lead to different findings concerning the effects of social media on student psychology in different countries. In other words, the potential positive and negative effects of popular social media like Facebook, Snapchat, Twitter, etc., on students’ affective conditions can differ across different educational settings in different host countries. Thus, significantly more research is needed in different contexts and cultures to compare the results.

There is also a need for further research on the higher education students and how their affective conditions are positively and negatively affected by the prevalent use of social media. University students’ psychological needs might be different from other academic grades and, thus, the patterns of changes that the overall use of social networking can create in their emotions can be also different. Their main reasons for using social media might be different from school students as well, which need to be investigated more thoroughly. The sorts of interventions needed to moderate the potential negative effects of social networking on them can be different too, all requiring a new line of research in education domain.

Finally, there are hopes that considering the ever-increasing popularity of social networking in education, the potential psychological effects of social media on teachers be explored as well. Though teacher psychology has only recently been considered for research, the literature has provided profound insights into teachers developing stress, motivation, self-esteem, and many other emotions. In today’s world driven by global communications in the cyberspace, teachers like everyone else are affecting and being affected by social networking. The comparison theory can hold true for teachers too. Thus, similar threats (of social media) to self-esteem and self-worth can be there for teachers too besides students, which are worth investigating qualitatively and quantitatively.

Probably a new line of research can be initiated to explore the co-development of teacher and learner psychological traits under the influence of social media use in longitudinal studies. These will certainly entail sophisticated research methods to be capable of unraveling the nuances of variation in these traits and their mutual effects, for example, stress, motivation, and self-esteem. If these are incorporated within mixed-approach works of research, more comprehensive and better insightful findings can be expected to emerge. Correlational studies need to be followed by causal studies in educational settings. As many conditions of the educational settings do not allow for having control groups or randomization, probably, experimental studies do not help with this. Innovative research methods, case studies or else, can be used to further explore the causal relations among the different features of social media use and the development of different affective variables in teachers or learners. Examples of such innovative research methods can be process tracing, qualitative comparative analysis, and longitudinal latent factor modeling (for a more comprehensive view, see Hiver and Al-Hoorie, 2019 ).

Author contributions

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

This study was sponsored by Wuxi Philosophy and Social Sciences bidding project—“Special Project for Safeguarding the Rights and Interests of Workers in the New Form of Employment” (Grant No. WXSK22-GH-13). This study was sponsored by the Key Project of Party Building and Ideological and Political Education Research of Nanjing University of Posts and Telecommunications—“Research on the Guidance and Countermeasures of Network Public Opinion in Colleges and Universities in the Modern Times” (Grant No. XC 2021002).

Conflict of interest

Author XX was employed by China Mobile Group Jiangsu Co., Ltd. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

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