research questions about social media usage

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• Published: 01 July 2020

The effect of social media on well-being differs from adolescent to adolescent

• Ine Beyens   ORCID: orcid.org/0000-0001-7023-867X 1 ,
• J. Loes Pouwels   ORCID: orcid.org/0000-0002-9586-392X 1 ,
• Irene I. van Driel   ORCID: orcid.org/0000-0002-7810-9677 1 ,
• Loes Keijsers   ORCID: orcid.org/0000-0001-8580-6000 2 &
• Patti M. Valkenburg   ORCID: orcid.org/0000-0003-0477-8429 1  

Scientific Reports volume  10 , Article number:  10763 ( 2020 ) Cite this article

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• Human behaviour

The question whether social media use benefits or undermines adolescents’ well-being is an important societal concern. Previous empirical studies have mostly established across-the-board effects among (sub)populations of adolescents. As a result, it is still an open question whether the effects are unique for each individual adolescent. We sampled adolescents’ experiences six times per day for one week to quantify differences in their susceptibility to the effects of social media on their momentary affective well-being. Rigorous analyses of 2,155 real-time assessments showed that the association between social media use and affective well-being differs strongly across adolescents: While 44% did not feel better or worse after passive social media use, 46% felt better, and 10% felt worse. Our results imply that person-specific effects can no longer be ignored in research, as well as in prevention and intervention programs.

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Introduction

Ever since the introduction of social media, such as Facebook and Instagram, researchers have been studying whether the use of such media may affect adolescents’ well-being. These studies have typically reported mixed findings, yielding either small negative, small positive, or no effects of the time spent using social media on different indicators of well-being, such as life satisfaction and depressive symptoms (for recent reviews, see for example 1 , 2 , 3 , 4 , 5 ). Most of these studies have focused on between-person associations, examining whether adolescents who use social media more (or less) often than their peers experience lower (or higher) levels of well-being than these peers. While such between-person studies are valuable in their own right, several scholars 6 , 7 have recently called for studies that investigate within-person associations to understand whether an increase in an adolescent’s social media use is associated with an increase or decrease in that adolescent’s well-being. The current study aims to respond to this call by investigating associations between social media use and well-being within single adolescents across multiple points in time 8 , 9 , 10 .

Person-specific effects

To our knowledge, four recent studies have investigated within-person associations of social media use with different indicators of adolescent well-being (i.e., life satisfaction, depression), again with mixed results 6 , 11 , 12 , 13 . Orben and colleagues 6 found a small negative reciprocal within-person association between the time spent using social media and life satisfaction. Likewise, Boers and colleagues 12 found a small within-person association between social media use and increased depressive symptoms. Finally, Coyne and colleagues 11 and Jensen and colleagues 13 did not find any evidence for within-person associations between social media use and depression.

Earlier studies that investigated within-person associations of social media use with indicators of well-being have all only reported average effect sizes. However, it is possible, or even plausible, that these average within-person effects may have been small and nonsignificant because they result from sizeable heterogeneity in adolescents’ susceptibility to the effects of social media use on well-being (see 14 , 15 ). After all, an average within-person effect size can be considered an aggregate of numerous individual within-person effect sizes that range from highly positive to highly negative.

Some within-person studies have sought to understand adolescents’ differential susceptibility to the effects of social media by investigating differences between subgroups. For instance, they have investigated the moderating role of sex to compare the effects of social media on boys versus girls 6 , 11 . However, such a group-differential approach, in which potential differences in susceptibility are conceptualized by group-level moderators (e.g., gender, age) does not provide insights into more fine-grained differences at the level of the single individual 16 . After all, while girls and boys each represent a homogenous group in terms of sex, they may each differ on a wide array of other factors.

As such, although worthwhile, the average within-person effects of social media on well-being obtained in previous studies may have been small or non-significant because they are diluted across a highly heterogeneous population (or sub-population) of adolescents 14 , 15 . In line with the proposition of media effects theories that each adolescent may have a unique susceptibility to the effects of social media 17 , a viable explanation for the small and inconsistent findings in earlier studies may be that the effect of social media differs from adolescent to adolescent. The aim of the current study is to investigate this hypothesis and to obtain a better understanding of adolescents’ unique susceptibility to the effects of social media on their affective well-being.

Social media and affective well-being

Within-person studies have provided important insights into the associations of social media use with cognitive well-being (e.g., life satisfaction 6 ), which refers to adolescents’ cognitive judgment of how satisfied they are with their life 18 . However, the associations of social media use with adolescents’ affective well-being (i.e., adolescents’ affective evaluations of their moods and emotions 18 ) are still unknown. In addition, while earlier within-person studies have focused on associations with trait-like conceptualizations of well-being 11 , 12 , 13 , that is, adolescents’ average well-being across specific time periods 18 , there is a lack of studies that focus on well-being as a momentary affective state. Therefore, we extend previous research by examining the association between adolescents’ social media use and their momentary affective well-being. Like earlier experience sampling (ESM) studies among adults 19 , 20 , we measured adolescents’ momentary affective well-being with a single item. Adolescents’ momentary affective well-being was defined as their current feelings of happiness, a commonly used question to measure well-being 21 , 22 , which has high convergent validity, as evidenced by the strong correlations with the presence of positive affect and absence of negative affect.

To assess adolescents’ momentary affective well-being (henceforth referred to as well-being), we conducted a week-long ESM study among 63 middle adolescents ages 14 and 15. Six times a day, adolescents were asked to complete a survey using their own mobile phone, covering 42 assessments per adolescent, assessing their affective well-being and social media use. In total, adolescents completed 2,155 assessments (83.2% average compliance).

We focused on middle adolescence, since this is the period in life characterized by most significant fluctuations in well-being 23 , 24 . Also, in comparison to early and late adolescents, middle adolescents are more sensitive to reactions from peers and have a strong tendency to compare themselves with others on social media and beyond. Because middle adolescents typically use different social media platforms, in a complementary way 25 , 26 , 27 , each adolescent reported on his/her use of the three social media platforms that s/he used most frequently out of the five most popular social media platforms among adolescents: WhatsApp, followed by Instagram, Snapchat, YouTube, and, finally, the chat function of games 28 . In addition to investigating the association between overall social media use and well-being (i.e., the summed use of adolescents’ three most frequently used platforms), we examined the unique associations of the two most popular platforms, WhatsApp and Instagram 28 .

Like previous studies on social media use and well-being, we distinguished between active social media use (i.e., “activities that facilitate direct exchanges with others” 29 ) and passive social media use (i.e., “consuming information without direct exchanges” 29 ). Within-person studies among young adults have shown that passive but not active social media use predicts decreases in well-being 29 . Therefore, we examined the unique associations of adolescents’ overall active and passive social media use with their well-being, as well as active and passive use of Instagram and WhatsApp, specifically. We investigated categorical associations, that is, whether adolescents would feel better or worse if they had actively or passively used social media. And we investigated dose–response associations to understand whether adolescents’ well-being would change as a function of the time they had spent actively or passively using social media.

The hypotheses and the design, sampling and analysis plan were preregistered prior to data collection and are available on the Open Science Framework, along with the code used in the analyses ( https://osf.io/nhks2 ). For details about the design of the study and analysis approach, see Methods.

In more than half of all assessments (68.17%), adolescents had used social media (i.e., one or more of their three favorite social media platforms), either in an active or passive way. Instagram (50.90%) and WhatsApp (53.52%) were used in half of all assessments. Passive use of social media (66.21% of all assessments) was more common than active use (50.86%), both on Instagram (48.48% vs. 20.79%) and WhatsApp (51.25% vs. 40.07%).

Strong positive between-person correlations were found between the duration of active and passive social media use (overall: r  = 0.69, p  < 0.001; Instagram: r  = 0.38, p  < 0.01; WhatsApp: r  = 0.85, p  < 0.001): Adolescents who had spent more time actively using social media than their peers, had also spent more time passively using social media than their peers. Likewise, strong positive within-person correlations were found between the duration of active and passive social media use (overall: r  = 0.63, p  < 0.001; Instagram: r  = 0.37, p  < 0.001; WhatsApp: r  = 0.57, p  < 0.001): The more time an adolescent had spent actively using social media at a certain moment, the more time s/he had also spent passively using social media at that moment.

Table 1 displays the average number of minutes that adolescents had spent using social media in the past hour at each assessment, and the zero-order between- and within-person correlations between the duration of social media use and well-being. At the between-person level, the duration of active and passive social media use was not associated with well-being: Adolescents who had spent more time actively or passively using social media than their peers did not report significantly higher or lower levels of well-being than their peers. At the within-person level, significant but weak positive correlations were found between the duration of active and passive overall social media use and well-being. This indicates that adolescents felt somewhat better at moments when they had spent more time actively or passively using social media (overall), compared to moments when they had spent less time actively or passively using social media. When looking at specific platforms, a positive correlation was only found for passive WhatsApp use, but not for active WhatsApp use, and not for active and passive Instagram use.

Average and person-specific effects

The within-person associations of social media use with well-being and differences in these associations were tested in a series of multilevel models. We ran separate models for overall social media use (i.e., active use and passive use of adolescents’ three favorite social media platforms, see Table 2 ), Instagram use (see Table 3 ), and WhatsApp use (see Table 4 ). In a first step we examined the average categorical associations for each of these three social media uses using fixed effects models (Models 1A, 3A, and 5A) to investigate whether, on average, adolescents would feel better or worse at moments when they had used social media compared to moments when they had not (i.e., categorical predictors: active use versus no active use, and passive use versus no passive use). In a second step, we examined heterogeneity in the within-person categorical associations by adding random slopes to the fixed effects models (Models 1B, 3B, and 5B). Next, we examined the average dose–response associations using fixed effects models (Models 2A, 4A, and 6A), to investigate whether, on average, adolescents would feel better or worse when they had spent more time using social media (i.e., continuous predictors: duration of active use and duration of passive use). Finally, we examined heterogeneity in the within-person dose–response associations by adding random slopes to the fixed effects models (Models 2B, 4B, and 6B).

Overall social media use.

The model with the categorical predictors (see Table 2 ; Model 1A) showed that, on average, there was no association between overall use and well-being: Adolescents’ well-being did not increase or decrease at moments when they had used social media, either in a passive or active way. However, evidence was found that the association of passive (but not active) social media use with well-being differed from adolescent to adolescent (Model 1B), with effect sizes ranging from − 0.24 to 0.68. For 44.26% of the adolescents the association was non-existent to small (− 0.10 <  r  < 0.10). However, for 45.90% of the adolescents there was a weak (0.10 <  r  < 0.20; 8.20%), moderate (0.20 <  r  < 0.30; 22.95%) or even strong positive ( r  ≥ 0.30; 14.75%) association between overall passive social media use and well-being, and for almost one in ten (9.84%) adolescents there was a weak (− 0.20 <  r  < − 0.10; 6.56%) or moderate negative (− 0.30 <  r  < − 0.20; 3.28%) association.

The model with continuous predictors (Model 2A) showed that, on average, there was a significant dose–response association for active use. At moments when adolescents had used social media, the time they spent actively (but not passively) using social media was positively associated with well-being: Adolescents felt better at moments when they had spent more time sending messages, posting, or sharing something on social media. The associations of the time spent actively and passively using social media with well-being did not differ across adolescents (Model 2B).

Instagram use

As shown in Model 3A in Table 3 , on average, there was a significant categorical association between passive (but not active) Instagram use and well-being: Adolescents experienced an increase in well-being at moments when they had passively used Instagram (i.e., viewing posts/stories of others). Adolescents did not experience an increase or decrease in well-being when they had actively used Instagram. The associations of passive and active Instagram use with well-being did not differ across adolescents (Model 3B).

On average, no significant dose–response association was found for Instagram use (Model 4A): At moments when adolescents had used Instagram, the time adolescents spent using Instagram (either actively or passively) was not associated with their well-being. However, evidence was found that the association of the time spent passively using Instagram differed from adolescent to adolescent (Model 4B), with effect sizes ranging from − 0.48 to 0.27. For most adolescents (73.91%) the association was non-existent to small (− 0.10 <  r  < 0.10), but for almost one in five adolescents (17.39%) there was a weak (0.10 <  r  < 0.20; 10.87%) or moderate (0.20 <  r  < 0.30; 6.52%) positive association, and for almost one in ten adolescents (8.70%) there was a weak (− 0.20 <  r  < − 0.10; 2.17%), moderate (− 0.30 <  r  < − 0.20; 4.35%), or strong ( r  ≤ − 0.30; 2.17%) negative association. Figure  1 illustrates these differences in the dose–response associations.

The dose–response association between passive Instagram use (in minutes per hour) and affective well-being for each individual adolescent (n = 46). Red lines represent significant negative within-person associations, green lines represent significant positive within-person associations, and gray lines represent non-significant within-person associations. A graph was created for each participant who had completed at least 10 assessments. A total of 13 participants were excluded because they had completed less than 10 assessments of passive Instagram use. In addition, one participant was excluded because no graph could be computed, since this participant's passive Instagram use was constant across assessments.

WhatsApp use

As shown in Model 5A in Table 4 , just as for Instagram, we found that, on average, there was a significant categorical association between passive (but not active) WhatsApp use and well-being: Adolescents reported that they felt better at moments when they had passively used WhatsApp (i.e., read WhatsApp messages). For active WhatsApp use, no significant association was found. Also, in line with the results for Instagram use, no differences were found regarding the associations of active and passive WhatsApp use (Model 5B).

In addition, a significant dose–response association was found for passive (but not active) use (Model 6A). At moments when adolescents had used WhatsApp, we found that, on average, the time adolescents spent passively using WhatsApp was positively associated with well-being: Adolescents felt better at moments when they had spent more time reading WhatsApp messages. The time spent actively using WhatsApp was not associated with well-being. No differences were found in the dose–response associations of active and passive WhatsApp use (Model 6B).

This preregistered study investigated adolescents’ unique susceptibility to the effects of social media. We found that the associations of passive (but not active) social media use with well-being differed substantially from adolescent to adolescent, with effect sizes ranging from moderately negative (− 0.24) to strongly positive (0.68). While 44.26% of adolescents did not feel better or worse if they had passively used social media, 45.90% felt better, and a small group felt worse (9.84%). In addition, for Instagram the majority of adolescents (73.91%) did not feel better or worse when they had spent more time viewing post or stories of others, whereas some felt better (17.39%), and others (8.70%) felt worse.

These findings have important implications for social media effects research, and media effects research more generally. For decades, researchers have argued that people differ in their susceptibility to the effects of media 17 , leading to numerous investigations of such differential susceptibility. These investigations have typically focused on moderators, based on variables such as sex, age, or personality. Yet, over the years, studies have shown that such moderators appear to have little power to explain how individuals differ in their susceptibility to media effects, probably because a group-differential approach does not account for the possibility that media users may differ across a range of factors, that are not captured by only one (or a few) investigated moderator variables.

By providing insights into each individual’s unique susceptibility, the findings of this study provide an explanation as to why, up until now, most media effects research has only found small effects. We found that the majority of adolescents do not experience any short-term changes in well-being related to their social media use. And if they do experience any changes, these are more often positive than negative. Because only small subsets of adolescents experience small to moderate changes in well-being, the true effects of social media reported in previous studies have probably been diluted across heterogeneous samples of individuals that differ in their susceptibility to media effects (also see 30 ). Several scholars have noted that overall effect sizes may mask more subtle individual differences 14 , 15 , which may explain why previous studies have typically reported small or no effects of social media on well-being or indicators of well-being 6 , 11 , 12 , 13 . The current study seems to confirm this assumption, by showing that while the overall effect sizes are small at best, the person-specific effect sizes vary considerably, from tiny and small to moderate and strong.

As called upon by other scholars 5 , 31 , we disentangled the associations of active and passive use of social media. Research among young adults found that passive (but not active) social media use is associated with lower levels of affective well-being 29 . In line with these findings, the current study shows that active and passive use yielded different associations with adolescents’ affective well-being. Interestingly though, in contrast to previous findings among adults, our study showed that, on average, passive use of Instagram and WhatsApp seemed to enhance rather than decrease adolescents’ well-being. This discrepancy in findings may be attributed to the fact that different mechanisms might be involved. Verduyn and colleagues 29 found that passive use of Facebook undermines adults’ well-being by enhancing envy, which may also explain the decreases in well-being found in our study among a small group of adolescents. Yet, adolescents who felt better by passively using Instagram and WhatsApp, might have felt so because they experienced enjoyment. After all, adolescents often seek positive content on social media, such as humorous posts or memes 32 . Also, research has shown that adolescents mainly receive positive feedback on social media 33 . Hence, their passive Instagram and WhatsApp use may involve the reading of positive feedback, which may explain the increases in well-being.

Overall, the time spent passively using WhatsApp improved adolescents’ well-being. This did not differ from adolescent to adolescent. However, the associations of the time spent passively using Instagram with well-being did differ from adolescent to adolescent. This discrepancy suggests that not all social media uses yield person-specific effects on well-being. A possible explanation may be that adolescents’ responses to WhatsApp are more homogenous than those to Instagram. WhatsApp is a more private platform, which is mostly used for one-to-one communication with friends and acquaintances 26 . Instagram, in contrast, is a more public platform, which allows its users to follow a diverse set of people, ranging from best friends to singers, actors, and influencers 28 , and to engage in intimate communication as well as self-presentation and social comparison. Such diverse uses could lead to more varied, or even opposing responses, such as envy versus inspiration.

Limitations and directions for future research

The current study extends our understanding of differential susceptibility to media effects, by revealing that the effect of social media use on well-being differs from adolescent to adolescent. The findings confirm our assumption that among the great majority of adolescents, social media use is unrelated to well-being, but that among a small subset, social media use is either related to decreases or increases in well-being. It must be noted, however, that participants in this study felt relatively happy, overall. Studies with more vulnerable samples, consisting of clinical samples or youth with lower social-emotional well-being may elicit different patterns of effects 27 . Also, the current study focused on affective well-being, operationalized as happiness. It is plausible that social media use relates differently with other types of well-being, such as cognitive well-being. An important next step is to identify which adolescents are particularly susceptible to experience declines in well-being. It is conceivable, for instance, that the few adolescents who feel worse when they use social media are the ones who receive negative feedback on social media 33 .

In addition, future ESM studies into the effects of social media should attempt to include one or more follow-up measures to improve our knowledge of the longer-term influence of social media use on affective well-being. While a week-long ESM is very common and applied in most earlier ESM studies 34 , a week is only a snapshot of adolescent development. Research is needed that investigates whether the associations of social media use with adolescents’ momentary affective well-being may cumulate into long-lasting consequences. Such investigations could help clarify whether adolescents who feel bad in the short term would experience more negative consequences in the long term, and whether adolescents who feel better would be more resistant to developing long-term negative consequences. And while most adolescents do not seem to experience any short-term increases or decreases in well-being, more research is needed to investigate whether these adolescents may experience a longer-term impact of social media.

While the use of different platforms may be differently associated with well-being, different types of use may also yield different effects. Although the current study distinguished between active and passive use of social media, future research should further differentiate between different activities. For instance, because passive use entails many different activities, from reading private messages (e.g., WhatsApp messages, direct messages on Instagram) to browsing a public feed (e.g., scrolling through posts on Instagram), research is needed that explores the unique effects of passive public use and passive private use. Research that seeks to explore the nuances in adolescents’ susceptibility as well as the nuances in their social media use may truly improve our understanding of the effects of social media use.

Participants

Participants were recruited via a secondary school in the south of the Netherlands. Our preregistered sampling plan set a target sample size of 100 adolescents. We invited adolescents from six classrooms to participate in the study. The final sample consisted of 63 adolescents (i.e., 42% consent rate, which is comparable to other ESM studies among adolescents; see, for instance 35 , 36 ). Informed consent was obtained from all participants and their parents. On average, participants were 15 years old ( M  = 15.12 years, SD  = 0.51) and 54% were girls. All participants self-identified as Dutch, and 41.3% were enrolled in the prevocational secondary education track, 25.4% in the intermediate general secondary education track, and 33.3% in the academic preparatory education track.

The study was approved by the Ethics Review Board of the Faculty of Social and Behavioral Sciences at the University of Amsterdam and was performed in accordance with the guidelines formulated by the Ethics Review Board. The study consisted of two phases: A baseline survey and a personalized week-long experience sampling (ESM) study. In phase 1, researchers visited the school during school hours. Researchers informed the participants of the objective and procedure of the study and assured them that their responses would be treated confidentially. Participants were asked to sign the consent form. Next, participants completed a 15-min baseline survey. The baseline survey included questions about demographics and assessed which social media each adolescent used most frequently, allowing to personalize the social media questions presented during the ESM study in phase 2. After completing the baseline survey, participants were provided detailed instructions about phase 2.

In phase 2, which took place two and a half weeks after the baseline survey, a 7-day ESM study was conducted, following the guidelines for ESM studies provided by van Roekel and colleagues 34 . Aiming for at least 30 assessments per participant and based on an average compliance rate of 70 to 80% reported in earlier ESM studies among adolescents 34 , we asked each participant to complete a total of 42 ESM surveys (i.e., six 2-min surveys per day). Participants completed the surveys using their own mobile phone, on which the ESM software application Ethica Data was installed during the instruction session with the researchers (phase 1). Each 2-min survey consisted of 22 questions, which assessed adolescents’ well-being and social media use. Two open-ended questions were added to the final survey of the day, which asked about adolescents’ most pleasant and most unpleasant events of the day.

The ESM sampling scheme was semi-random, to allow for randomization and avoid structural patterns in well-being, while taking into account that adolescents were not allowed to use their phone during school time. The Ethica Data app was programmed to generate six beep notifications per day at random time points within a fixed time interval that was tailored to the school’s schedule: before school time (1 beep), during school breaks (2 beeps), and after school time (3 beeps). During the weekend, the beeps were generated during the morning (1 beep), afternoon (3 beeps), and evening (2 beeps). To maximize compliance, a 30-min time window was provided to complete each survey. This time window was extended to one hour for the first survey (morning) and two hours for the final survey (evening) to account for travel time to school and time spent on evening activities. The average compliance rate was 83.2%. A total of 2,155 ESM assessments were collected: Participants completed an average of 34.83 surveys ( SD  = 4.91) on a total of 42 surveys, which is high compared to previous ESM studies among adolescents 34 .

The questions of the ESM study were personalized based on the responses to the baseline survey. During the ESM study, each participant reported on his/her use of three different social media platforms: WhatsApp and either Instagram, Snapchat, YouTube, and/or the chat function of games (i.e., the most popular social media platforms among adolescents 28 ). Questions about Instagram and WhatsApp use were only included if the participant had indicated in the baseline survey that s/he used these platforms at least once a week. If a participant had indicated that s/he used Instagram or WhatsApp (or both) less than once a week, s/he was asked to report on the use of Snapchat, YouTube, or the chat function of games, depending on what platform s/he used at least once a week. In addition to Instagram and WhatsApp, questions were asked about a third platform, that was selected based on how frequently the participant used Snapchat, YouTube, or the chat function of games (i.e., at least once a week). This resulted in five different combinations of three platforms: Instagram, WhatsApp, and Snapchat (47 participants); Instagram, WhatsApp, and YouTube (11 participants); Instagram, WhatsApp, and chatting via games (2 participants); WhatsApp, Snapchat, and YouTube (1 participant); and WhatsApp, YouTube, and chatting via games (2 participants).

Frequency of social media use

In the baseline survey, participants were asked to indicate how often they used and checked Instagram, WhatsApp, Snapchat, YouTube, and the chat function of games, using response options ranging from 1 ( never ) to 7 ( more than 12 times per day ). These platforms are the five most popular platforms among Dutch 14- and 15-year-olds 28 . Participants’ responses were used to select the three social media platforms that were assessed in the personalized ESM study.

Duration of social media use

In the ESM study, duration of active and passive social media use was measured by asking participants how much time in the past hour they had spent actively and passively using each of the three platforms that were included in the personalized ESM surveys. Response options ranged from 0 to 60 min , with 5-min intervals. To measure active Instagram use, participants indicated how much time in the past hour they had spent (a) “posting on your feed or sharing something in your story on Instagram” and (b) “sending direct messages/chatting on Instagram.” These two items were summed to create the variable duration of active Instagram use. Sum scores exceeding 60 min (only 0.52% of all assessments) were recoded to 60 min. To measure duration of passive Instagram use, participants indicated how much time in the past hour they had spent “viewing posts/stories of others on Instagram.” To measure the use of WhatsApp, Snapchat, YouTube and game-based chatting, we asked participants how much time they had spent “sending WhatsApp messages” (active use) and “reading WhatsApp messages” (passive use); “sending snaps/messages or sharing something in your story on Snapchat” (active use) and “viewing snaps/stories/messages from others on Snapchat” (passive use); “posting YouTube clips” (active use) and “watching YouTube clips” (passive use); “sending messages via the chat function of a game/games” (active use) and “reading messages via the chat function of a game/games” (passive use). Duration of active and passive overall social media use were created by summing the responses across the three social media platforms for active and passive use, respectively. Sum scores exceeding 60 min (2.13% of all assessments for active overall use; 2.90% for passive overall use) were recoded to 60 min. The duration variables were used to investigate whether the time spent actively or passively using social media was associated with well-being (dose–response associations).

Use/no use of social media

Based on the duration variables, we created six dummy variables, one for active and one for passive overall social media use, one for active and one for passive Instagram use, and one for active and one for passive WhatsApp use (0 =  no active use and 1 =  active use , and 0 =  no passive use and 1 =  passive use , respectively). These dummy variables were used to investigate whether the use of social media, irrespective of the duration of use, was associated with well-being (categorical associations).

Consistent with previous ESM studies 19 , 20 , we measured affective well-being using one item, asking “How happy do you feel right now?” at each assessment. Adolescents indicated their response to the question using a 7-point scale ranging from 1 ( not at all ) to 7 ( completely ), with 4 ( a little ) as the midpoint. Convergent validity of this item was established in a separate pilot ESM study among 30 adolescents conducted by the research team of the fourth author: The affective well-being item was strongly correlated with the presence of positive affect and absence of negative affect (assessed by a 10-item positive and negative affect schedule for children; PANAS-C) at both the between-person (positive affect: r  = 0.88, p < 0.001; negative affect: r  = − 0.62, p < 0.001) and within-person level (positive affect: r  = 0.74, p < 0.001; negative affect: r  = − 0.58, p < 0.001).

Statistical analyses

Before conducting the analyses, several validation checks were performed (see 34 ). First, we aimed to only include participants in the analyses who had completed more than 33% of all ESM assessments (i.e., at least 14 assessments). Next, we screened participants’ responses to the open questions for unserious responses (e.g., gross comments, jokes). And finally, we inspected time series plots for patterns in answering tendencies. Since all participants completed more than 33% of all ESM assessments, and no inappropriate responses or low-quality data patterns were detected, all participants were included in the analyses.

Following our preregistered analysis plan, we tested the proposed associations in a series of multilevel models. Before doing so, we tested the homoscedasticity and linearity assumptions for multilevel analyses 37 . Inspection of standardized residual plots indicated that the data met these assumptions (plots are available on OSF at  https://osf.io/nhks2 ). We specified separate models for overall social media use, use of Instagram, and use of WhatsApp. To investigate to what extent adolescents’ well-being would vary depending on whether they had actively or passively used social media/Instagram/WhatsApp or not during the past hour (categorical associations), we tested models including the dummy variables as predictors (active use versus no active use, and passive use versus no passive use; models 1, 3, and 5). To investigate whether, at moments when adolescents had used social media/Instagram/WhatsApp during the past hour, their well-being would vary depending on the duration of social media/Instagram/WhatsApp use (dose–response associations), we tested models including the duration variables as predictors (duration of active use and duration of passive use; models 2, 4, and 6). In order to avoid negative skew in the duration variables, we only included assessments during which adolescents had used social media in the past hour (overall, Instagram, or WhatsApp, respectively), either actively or passively. All models included well-being as outcome variable. Since multilevel analyses allow to include all available data for each individual, no missing data were imputed and no data points were excluded.

We used a model building approach that involved three steps. In the first step, we estimated an intercept-only model to assess the relative amount of between- and within-person variance in affective well-being. We estimated a three-level model in which repeated momentary assessments (level 1) were nested within adolescents (level 2), who, in turn, were nested within classrooms (level 3). However, because the between-classroom variance in affective well-being was small (i.e., 0.4% of the variance was explained by differences between classes), we proceeded with estimating two-level (instead of three-level) models, with repeated momentary assessments (level 1) nested within adolescents (level 2).

In the second step, we assessed the within-person associations of well-being with (a) overall active and passive social media use (i.e., the total of the three platforms), (b) active and passive use of Instagram, and (c) active and passive use of WhatsApp, by adding fixed effects to the model (Models 1A-6A). To facilitate the interpretation of the associations and control for the effects of time, a covariate was added that controlled for the n th assessment of the study week (instead of the n th assessment of the day, as preregistered). This so-called detrending is helpful to interpret within-person associations as correlated fluctuations beyond other changes in social media use and well-being 38 . In order to obtain within-person estimates, we person-mean centered all predictors 38 . Significance of the fixed effects was determined using the Wald test.

In the third and final step, we assessed heterogeneity in the within-person associations by adding random slopes to the models (Models 1B-6B). Significance of the random slopes was determined by comparing the fit of the fixed effects model with the fit of the random effects model, by performing the Satorra-Bentler scaled chi-square test 39 and by comparing the Bayesian information criterion (BIC 40 ) and Akaike information criterion (AIC 41 ) of the models. When the random effects model had a significantly better fit than the fixed effects model (i.e., pointing at significant heterogeneity), variance components were inspected to investigate whether heterogeneity existed in the association of either active or passive use. Next, when evidence was found for significant heterogeneity, we computed person-specific effect sizes, based on the random effect models, to investigate what percentages of adolescents experienced better well-being, worse well-being, and no changes in well-being. In line with Keijsers and colleagues 42 we only included participants who had completed at least 10 assessments. In addition, for the dose–response associations, we constructed graphical representations of the person-specific slopes, based on the person-specific effect sizes, using the xyplot function from the lattice package in R 43 .

Three improvements were made to our original preregistered plan. First, rather than estimating the models with multilevel modelling in R 43 , we ran the preregistered models in Mplus 44 . Mplus provides standardized estimates for the fixed effects models, which offers insight into the effect sizes. This allowed us to compare the relative strength of the associations of passive versus active use with well-being. Second, instead of using the maximum likelihood estimator, we used the maximum likelihood estimator with robust standard errors (MLR), which are robust to non-normality. Sensitivity tests, uploaded on OSF ( https://osf.io/nhks2 ), indicated that the results were almost identical across the two software packages and estimation approaches. Third, to improve the interpretation of the results and make the scales of the duration measures of social media use and well-being more comparable, we transformed the social media duration scores (0 to 60 min) into scales running from 0 to 6, so that an increase of 1 unit reflects 10 min of social media use. The model estimates were unaffected by this transformation.

Reporting summary

Further information on the research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

The dataset generated and analysed during the current study is available in Figshare 45 . The preregistration of the design, sampling and analysis plan, and the analysis scripts used to analyse the data for this paper are available online on the Open Science Framework website ( https://osf.io/nhks2 ).

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Acknowledgements

This study was funded by the NWO Spinoza Prize and the Gravitation grant (NWO Grant 024.001.003; Consortium on Individual Development) awarded to P.M.V. by the Dutch Research Council (NWO). Additional funding was received from the VIDI grant (NWO VIDI Grant 452.17.011) awarded to L.K. by the Dutch Research Council (NWO). The authors would like to thank Savannah Boele (Tilburg University) for providing her pilot ESM results.

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I.B., J.L.P., I.I.v.D., L.K., and P.M.V. designed the study; I.B., J.L.P., and I.I.v.D. collected the data; I.B., J.L.P., and L.K. analyzed the data; and I.B., J.L.P., I.I.v.D., L.K., and P.M.V. contributed to writing and reviewing the manuscript.

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Beyens, I., Pouwels, J.L., van Driel, I.I. et al. The effect of social media on well-being differs from adolescent to adolescent. Sci Rep 10 , 10763 (2020). https://doi.org/10.1038/s41598-020-67727-7

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A mixed-methods study of problematic social media use, attention dysregulation, and social media use motives

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Problematic social media use (PSMU) refers to excessive uncontrolled use of social media which impacts upon daily functioning (Blackwell et al., 2017 ). Self-regulation is central to the development and experience of PSMU, and conceptually interrelates with individual usage motivations (Reinecke et al., 2022 ). While there is a growing body of research on social media use motivations, how usage motivations and self-regulation combined influence PSMU is not well understood. There are also persistent questions around the effectiveness of addiction-based measures of PSMU. The quantitative component of this nested mixed-methods study (N = 607) employed hierarchical regression and structural equation modelling, principally identifying that impulsive social media usage mediates the pathway between perceived executive/attentional functioning and the Bergen Social Media Addiction Scale (BSMAS, Andreassen et al., 2012 , 2016 ), a popular tool used to measure PSMU. In contrast, social-engagement motivations had a negative influence on the BSMAS. The qualitative component, comprising interview/open-ended questionnaire, explored individual experiences self-regulating social media use. Participants (N = 24) were recruited from the survey study, based on meeting screening criteria for executive dysfunction (Adult Self-Report ADHD Scale, Kessler et al., 2005 ), with sub-groups defined by top and bottom quartile BSMAS scores (evenly grouped). Thematic analysis found that most individuals with attention dysregulation, regardless of their BSMAS category, perceive self-regulation of social media use as highly challenging and effortful, describing broadly problematic relationship with social media. They also described rich combination of motivations and context of using social media, and strategies for managing use. This research questions the effectiveness of the BSMAS as a measure of general PSMU (lacking a formed self-regulation component), especially in individuals with attentional dysregulation. Future research investigating self-regulation strategies and focusing on characteristics of positive social media use is needed.

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Introduction

Over the past two decades, social media use has become an increasingly popular way to stay in contact with friends and family, find entertainment and pass time. Social media refers to internet-based sites and applications on which users can create public or semi-public profiles, interact with others, and share user-generated content (Boyd & Ellison, 2007 ; Paakkari et al., 2021 ). Approximately 90% of emerging adults, aged 18 to 29 years, report using social media daily, and a majority have active accounts on at least five different social media platforms (Scott et al., 2017 ). Furthermore, the global average time spent per day engaging with social media is increasing steadily, from a reported 1.5 h in 2012 to almost 2.5 h in 2020 (Tankovska, 2021 ). In a recent study by Schivinski and colleagues ( 2020 ), over 96% of participants reported accessing social media via a smartphone, and the most frequently used social media platforms in order were: Facebook, Instagram, Twitter and Snapchat.

Social media helps to form and maintain social connections, and to create supportive communities for individuals from diverse groups, including LGBT + adolescents, individuals from ethnic minorities and those with chronic illnesses (Shapiro & Margolin, 2014 ; Van Den Eijnden et al., 2018 ). A qualitative study by Radovic and colleagues ( 2017 ), which explored the positive and negative uses of social media in adolescents with depression, reported that some depressed teens use social media to seek encouragement and inspiration or for self-reflection via a private online journal. Further benefits of social media use include increased perceived social support and decreased loneliness (Best et al., 2014 ; Lee et al., 2013 ). However, despite many benefits of social media, popular narrative often focuses on the negative outcomes for users, which is reflected in the research literature (Owenz, n.d. ).

Problematic Social Media Use

Problematic social media use (PSMU) refers to the excessive, uncontrolled use of social media platforms that leads to detrimental effects on the users’ functioning and wellbeing (Kuss & Griffiths, 2017 ; Blackwell et al., 2017 ). Schivinski and colleagues ( 2020 ) reported PSMU at a prevalence of 6.68% of social media users, while other studies have estimated that 7–11% of adolescents are problematic users (van den Eijnden et al., 2016 ; Eijnden et al., 2018 ). In their recent study, Paakkari and colleagues ( 2021 ) reported an additional 33.5% of social media users as at moderate risk of developing problematic use of social media, a large proportion of whom were young women with low parental monitoring. Paakkari et al. further report health and wellbeing impacts associated with PSMU, including headaches, neck pain, shortened sleep, irritability, nervousness, and loneliness. Similarly, previous research has documented links between PSMU and indicators of mental health such as anxiety, depressive symptoms, and lower self-esteem (Andreassen, 2015 ), decreased psychological wellbeing and life satisfaction (van den Eijnden et al., 2018 ; Wang et al., 2016 ), and poorer outcomes in academic study, as it was found to be a significant predictor of lower GPA (van den Eijnden et al., 2018 ) and academic procrastination (Yildiz Durak, 2020 ; Lian et al., 2018) in high school and undergraduate students respectively. Furthermore, a systematic review by Kokka and colleagues ( 2021 ) found that problematic internet use is associated with disrupted sleep patterns, shortened sleep time and poorer sleep quality. The severity of problematic internet use was also found to be negatively related to psychological wellbeing (Mei et al., 2016 ) and is linked with depressive symptoms and feelings of loneliness (Vigna-Taglianti et al., 2017 ).

However, there is a growing call for researchers to adopt a more cautious and critical approach to PSMU, noting similar patterns of research on negative impacts (followed by more conservative revision) in other areas of new technology use, such as with internet use and online gaming (Ellis, 2019 ; Aarseth et al., 2017 ). Of particular impact, Orben and Prybylski ( 2019 ) argue that many of the strong claims about the negative impacts of technology use are driven by flawed assumptions that large-scale empirical data ensures robust conclusions. They evaluated the impact of technology use (including social media use) on adolescent psychological wellbeing, sourcing data from three large national longitudinal health and wellbeing surveys from the US and UK. The authors employed Specification Curve Analysis, a statistical approach that minimizes the impact of researcher decisions on the selection of variables and their relationships for analysis, especially problematic in large cohort data with many variables where small effects can generate significant results. Orben and Prybylski report that while social media use had a negative association with wellbeing, the effect was small, accounting for less than 0.1% of the variability in wellbeing. They further contextualize the findings by noting that the overall impact of technology use on wellbeing was substantially less than the impacts on wellbeing associated with other common adolescent experiences, such as being bullied and binge drinking, and ranked somewhat more closely to the association for eating potatoes. While sobering, the authors note that issues with the measures of technology use limit the generalisability of the data. This is particularly true of social media use, which is measured in terms of self-report hours of usage.

Time spent on social media may correlate with PSMU as problematic users in general are probably more likely to spend time on social media, but there is much variance in simple usage characteristics. For instance, highly engaged productive activities like hosting a YouTube channel would naturally result in much of one’s time being spent on and thinking about social media, but this would be a very poor indicator of whether such an individual’s use was problematic or not. This is borne out in the research, with recent meta-analysis showing that “screen time”, including time spent on social media specifically, was unrelated to mental health outcomes (Ferguson et al., 2022 ). In research on PSMU, Yildiz Durak ( 2020 ) found no statistically significant relationship between Social Media Disorder Scale (van den Eijnden et al., 2016 ) and the duration of time spent on social media per day in a sample of 451 Turkish high school students. Similar results are reported by Boer et al. ( 2021 ) in an investigation involving 2,109 Dutch high school students. Indeed, van den Eijnden and colleagues ( 2018 ) distinguish between heavy social media use and addictive social media use. They found positive social outcomes, including the formation and development of friendships, for individuals in the heavy social media use group, compared with decreased psychological well-being and life satisfaction for individuals in the addicted social media use group.

It is noteworthy that much of the research on PSMU is presented in or traces back to an addiction (or use disorder) framework. Popular measures, such as the Bergen Social Media Addiction Scale (BSMAS; Andreassen et al., 2016 ) and the Social Media Disorder Scale (SMDS; van den Eijnden et al., 2016 ) correspond to diagnostic themes of addiction: salience, craving, mood modification, escape, withdrawal, and conflict (Andreassen et al., 2016 ). While social media addiction (SMA) has gained traction and is a fast-growing research area (Sun and Zhang, 2021), there are important limitations that are not routinely considered in research on PSMU/SMA. Conceptually, as with internet and smartphone use, social media use involves a wide range of possible behaviours and activities, and it is not clear how these various kinds of behaviours articulate with general measures of SMA (Lee et al., 2017 ; Carbonell & Panova, 2017 ).

Despite the wealth of research on PSMU/SMA, much of it is considered as being at the stage of initial screening research, being correlational, cross-sectional, self-report studies focusing on young adult university students (Carbonell & Panova, 2017 ; Ellis, 2019 ). Carbonell & Panova further highlight that research using such screening tools (e.g., the BSMAS) are fraught with false positives, as they normally do not indicate specific behaviours that can be used for validity testing, and there is no clinical definition that can be used as a gold standard. Moreover, there are concerns with survey-based addiction screening tools, which tend to have low predictive value, especially where the prevalence of a particular disorder is low (Maraz et al., 2015 ), and as such, the tools should only be used as an early detection mechanism, not to draw conclusions around the nature of the construct or to identify whether certain behaviours are pathological. Also, and perhaps most importantly, the addiction framework has much potential to problematize normal behaviour, without necessarily predicting problematic behaviours or outcomes (see Aarseth et al. 2016 for a discussion of this related to gaming, and Ellis 2019 in relation to smartphone use, and Carbonell & Panova for similar discussion of social media).

Importance of self-regulation

Whilst there are some deep challenges in the area of SMA, and consequently within PSMU, there is a growing recognition of the centrality of self-regulation to understanding PSMU, echoing similar trends in other areas of human-technology interaction, such as smartphone use (Busch & McCarthy, 2021 ), and internet use (Kumar Sinha et al., 2020 ; Mei et al., 2016 ). In a study of adolescent PSMU, self-regulation was negatively related to PSMU, such that adolescents who were able to regulate their social media use had a decreased likelihood of developing PSMU (Yildiz Durak, 2020 ). Indeed, Reinecke et al. ( 2022 ) argue that self-regulation is a key boundary condition, distinguishing between problematic and non-problematic use.

When considering the role of self-regulation, the context of attention deficit hyperactivity disorder (ADHD) presents as an interesting focus as it is clinically characterised by difficulties with self-regulation (inattention, hyperactivity, and impulsivity; American Psychiatric Association, 2013 ). It is important to recognise that the experience of ADHD (indeed neurodiversity and other differences generally), should not be discussed solely in terms of the limitations imposed by a deficit model (Dinishak, 2016 ). The environments within which most people with ADHD are required to function are often not conducive to self-regulation, however, and so the challenges to self-regulation (and capacities thereof) tend to be highly salient. Unsurprisingly, ADHD has been positively correlated with PSMU (Andreassen et al., 2016 ; Hussain & Griffiths, 2021 ; Merelle et al., 2017 ; Ra et al., 2018 ), and with related areas of problematic internet and smartphone use (Cakmak & Gul, 2018 ; Demirtaş et al., 2020 ; Evren et al., 2018 ; Panagiotidi & Overton, 2020 ; Wang et al., 2017 ). In a recent longitudinal study investigating ADHD symptoms and social media use, Boer and colleagues ( 2020 ) found greater social media use intensity and social media use problems to be correlated with greater ADHD symptoms in a sample of 543 Dutch adolescents. Similar findings were reported by Ra et al. in a diary study, and while not focused on ADHD, Du et al. (2021) tracked 329 adult users of social media over 4 months and identified a reciprocal relationship between mindful awareness (which reflects processes of self-regulation) and self-control failure, such that self-control failures impaired mindful awareness, which in turn increased future self-control failures.

Though more research is required, self-regulation is clearly important for individual control of social media use, and it has been suggested that subclinical ADHD inattention symptoms contribute to PSMU and problematic internet use (Panagiotidi & Overton, 2020 ). Lee and colleagues ( 2021 ) explored the relationship between inattention and PSMU in their study of functional connectivity differences between problematic and non-problematic social media users within the dorsal attention network and dorsolateral prefrontal cortex (DLPFC). The dorsal attention network is responsible for top-down control of attention and the DLPFC is suggested to be involved in executive control (Ceranoglu, 2018 ; Lee et al., 2021 ). Lee and colleagues ( 2021 ) found that problematic users of social media had weaker functional connectivity between these two regions, indicating deficits in prefrontal attention control, which contribute to poor self-regulation of social media use. Whilst the cause of failures in self-regulation are complex, individual hedonic state and tolerance to boredom may be an important mechanism. Indeed, there is an unsurprising link between PSMU and boredom proneness (Stockdale & Coyne, 2020 ), which has also been shown in the context of problematic smartphone use (Elhai et al., 2018 ), with Stockdale and Coyne also noting that it is a trait characteristic of ADHD.

Social Media Use Motives

Motivations for using social media, such as the alleviation of boredom or for procrastination, have been found to be associated with PSMU, social and psychological outcomes, and characteristics of social media use (Korhan & Ersoy, 2016 ; Meier, 2022 ; Omar & Dequan, 2020 ; Schivinski et al., 2020 ). Uses and gratifications theory, developed in communications research for media such as radio and newspaper and naturally extended to new media, considers the individual’s active choice to engage with media to satisfy their social and psychological needs (Dolan et al., 2016 ; Whiting & Williams, 2013 ). In essence, individuals will engage with social media to the extent that features and affordances fulfil their needs and desires (Liu and colleagues, 2020 ). Social media use motives vary between studies but typically include some form of the following: (a) information seeking, (b) social connection, (c) entertainment, (d) escapism, (e) self-expression and (f) surveillance (Dolan et al., 2016 ; Korhan & Ersoy, 2016 ; Omar & Dequan, 2020 ; Schivinski et al., 2020 ; Süral et al., 2019 ; Wang et al., 2016 ; Whiting & Williams, 2013 ).

Social media use motives have been associated with a variety of outcomes related to PSMU, including psychological distress, depression, and anxiety (Stockdale & Coyne, 2020 ). For example, Rae & Lonborg ( 2015 ) found that Facebook users who used the social media platform primarily to form new social connections had significantly higher levels of depression, anxiety and loss of behavioural/emotional control, than those who use social media to maintain existing relationships. In contrast, using social media with the motivation of seeking information is not associated with negative outcomes in mental health or behaviour (Stockdale & Coyne, 2020 ). Moreover, self-expression and passing time have been found to be related to excessive use of social media, both for individuals who engage in excessive use of Weibo and those who do not (Wang et al., 2016 ). Similarly, Süral and colleagues ( 2019 ) found that self-presentation and escapism may lead to PSMU.

Given that motives and characteristics of social media use define how we engage with social media, such motives may provide a link to understanding the development of PSMU. Indeed, a growing body of research demonstrates various roles that use motives have on productive and problematic use. However, there is a gap in understanding the role of self-regulation in the interplay of social media motives and PSMU. The present study uses a nested mixed methods design to: (1) investigate the relationship between PSMU, attention dysregulation, and social media use motives (entertainment, information seeking, social interaction, procrastination, stress relief, and unintentional/habitual use), along with indicators of psychological wellbeing (anxiety, depression, and stress); and (2) explore the experiences of social media use in university students with indicators of attention dysregulation. We expect that attention dysregulation will be a significant predictor of PSMU, and that both attention dysregulation and PSMU will be positively related to social media use motives of procrastination, and unintentional habitual use, while purposeful adaptive use motives (social, information seeking, entertainment, and stress relief) may reduce PSMU. The ambiguous role of wellbeing in PSMU makes it unclear what role it may take. The qualitative component will explore the perspectives and experiences of participants in view of their ability to self-regulate social media use and their motivations to use social media.

The current study implemented a nested mixed methods design, allowing for deeper investigation into attention dysregulation and social media use motives as related to PSMU. Quantitative methods were Structural Equation Modelling, guided by Exploratory Factor Analysis and hierarchical regression, to investigate the influence of attention dysregulation and social media use motives on PSMU. Interviews and open-ended questionnaires were used to explore the experiences of individuals with indicators of attention dysregulation when self-regulating social media use and their motivations to use social media. Ethics approval was obtained through the Western Sydney University Human Research Ethics Committee prior to commencement of the study.

Participants

Participants were recruited from a pool of first-year psychology students through the Western Sydney University SONA portal. Participants were incentivised to complete the 30-minute survey with a course credit reward. Informed consent was gained from participants prior to commencing the survey. Within the survey, participants were asked about their social media use and completed self-report measures of ADHD symptoms, psychological wellbeing (depression, anxiety, and stress), PSMU, and social media motivations.

The interview pool was selected based upon two criteria: (1) a clinical ASRS score of 5 or greater, and (2) a score within the highest or lowest quartile on the BSMAS, to ensure a range of social media use experiences in the sample. Participants who met these criteria were emailed an invitation to complete an interview or an equivalent open-ended questionnaire. Interviews were conducted via video call and lasted on average 20 min. Prior to the interview, participants consented to being recorded and for their responses to be used in the study. Interview recordings were transcribed for thematic analysis. Alternatively, participants could opt to complete an open-ended questionnaire on Qualtrics in the place of an interview. Participation in the interview or open-ended questionnaire was incentivised with further course credit.

Participants for the quantitative study

A total of 703 first-year psychology students, recruited from amongst the 2021 Autumn and Spring cohorts at Western Sydney University. Following data screening, six participants were omitted from the dataset as multivariate outliers, while an additional 90 were flagged as low effort responders. Therefore, 607 participants were included in this study, ranging in age from 17 to 58 (M = 22.02, SD = 6.179). The sample was made up predominantly of individuals identifying as cisgender women (475 women, 126 men, 4 non-binary, 2 undisclosed).

Participants for the qualitative study

From the initial sample, 24 participants between ages 18 and 31 (19 women, 5 men, M = 20.8, SD = 3.9) volunteered to join the qualitative study, completing either an interview (n = 7) or equivalent open-ended questionnaire (n = 17).

Quantitative study

Bergen social media addiction scale (bsmas).

The BSMAS (Andreassen et al., 2012 , 2016 ) was used as a measure of PSMU as it is a popular, brief (6-item) measure. It is scored on a five-point scale from 1 (very rarely) to 5 (very often), framing six features of purportedly addictive use: salience, tolerance, mood modification, relapse, withdrawal, and conflict. We adopted the more common total sum scoring approach, rather than dichotomous cut-off scoring. The BSMAS is a widely used measure within PSMU research with research claiming validity (e.g., Balcerowska et al., 2022 ), however, there is some evidence of structural issues, such as Watson et al. ( 2020 ) indicating problematic fit and an (unspecified) item failing to load with the others.

Adult ADHD Self-Report Scale (ASRS)

The ASRS (Kessler et al., 2005 ) was used as a measure of attention dysregulation via ADHD symptoms. This measure has been used in both clinical and research settings as a screening tool for ADHD and has been found to have high validity and accuracy. It is a six-item measure in which participants respond to questions about how frequently they experienced the given symptom in the past six months. Responses are given on a five-point scale from 1 (never) to 5 (very often) and scores are summed to form a total score. A clinical cut-off is assigned to each item and participants are scored either 0 (doesn’t meet cut-off) or 1 (meets cut-off). These scores are summed to form a clinical ASRS score. The authors present evidence for convergent and discriminant validity, and test-retest reliability has been demonstrated in non-ADHD samples (Silverstein et al., 2018 ).

Social Media Motivations

Participants completed an ad-hoc 12-item measure of social media use motives, responding on a five-point scale how closely their motivations to use social media aligned with given statements, from 1 (not at all) to 5 (exactly). An average for each use motive is then calculated. Informed by research in the use and gratification area (e.g., Whiting & Williams 2013 , who reported categories of uses most cited by respondents), we developed items assessing the following use characteristics: entertainment (e.g., I use social media because it is fun); social interaction (e.g., I sue social media to engage with other people); information seeking (e.g., I use social media for study/research); stress management (“I use social media to deal with stress”); procrastination (e.g., “I use social media even though I have more important things to do”); and habit (e.g., “I start using social media before I consciously realise I’m doing it”). Two questions are aligned with each category. While other researchers have used more extensive measures of use and gratification motivations (see for instance Lee & Kim 2014 , who delve into uses such as surveillance and network expansion), we focus on broader motivational categories or characteristics of usage.

Depression Anxiety Stress Scale − 21 (DASS-21)

The DASS-21 (Lovibond & Lovibond, 1995 ) was used as a measure of depression and anxiety symptoms. The DASS-21 is a frequently used measure in both clinical and research settings and has demonstrated excellent validity and internal consistency (Antony et al., 1998 ). It is a 21-item measure composed of three subscales: depression (ɑ = 0.97), anxiety (ɑ = 0.92), and stress (ɑ = 0.95). Participants respond on a five-point scale from 1 (does not apply to me at all) to 5 (applied to me very much, or most of the time) to statements about how they have felt in the last week. Scores are then summed to form three total scores, one for each subscale. Psychometric qualities of this scale have been established, with adequate construct validity in non-clinical samples (Henry & Crawford, 2005 ).

Qualitative study

Participants completed either a semi-structured interview via video call or an open-ended questionnaire through Qualtrics which explored their experiences with social media use. Participants responded to questions about: (a) their social media use, (b) their motivations to use social media, (c) the impact of social media use on their lives, and (d) their experiences of self- regulating or reducing social media use. A copy of the open-ended questionnaire is included in Appendix A, the live interviews followed the same protocol and included the same prompts, but with differences naturally emerging depending on individual conversational dynamics.

Data Analysis

Quantitative study – initial screening.

The survey data was screened for outliers and violations of parametric assumptions using SPSS (IBM SPSS Version 25.0, 2017). Univariate analysis found no abnormalities and no participants were omitted from the dataset as univariate outliers. The dataset was screened for multivariate outliers using Mahalanobis distance and six participants were omitted as multivariate outliers. Additionally, adapting recommendations in the psychometric literature (Meade & Craig, 2012 ; Niessen et al., 2016 ), 90 participants that displayed evidence of insufficient effort responding were flagged as potentially unreliable data; this included those participants who spent an improbably low time on the survey (< 10 min, based on initial pre-testing), and those who demonstrated low odd/even test reliability. Removing these participants did not change the overall outcomes, but did improve model strength, and so this is the data reported. The parametric assumptions of multiple regression were satisfactory, which offers initial confidence for SEM. The assumptions of normality, linearity and homoscedasticity were checked through a scatterplot of residuals and predicted values. There was no evidence of multicollinearity with acceptable VIF scores and no correlation between predictor variables > 0.9.

Qualitative study - approach

Interview transcripts and written responses from open-ended questionnaires were screened and cleaned, then analysed according to Braun and Clarke’s ( 2006 ) six steps of thematic analysis. Broadly, this involved familiarisation with the dataset, initial coding, refining of codes, grouping of concepts and finally forming themes. An inductive approach was utilised in this study, with themes developed from the data. This process produced two themes and seven subthemes.

Quantitative results

A core aim of this research is to investigate the relationship between PSMU, attention dysregulation, and social media use motives (entertainment, information seeking, social interaction, procrastination, stress relief, and unintentional habitual use). We expect that individual characteristics of social media usage will differentially influence PSMU, in particular: exacerbated by impulsive characteristics, and mitigated by engaged and social usage. A Structural Equation Modelling (SEM) approach would provide a powerful tool to investigate these relationships, as long as the data satisfies SEM requirements. It was deemed prudent to commence with an Exploratory Factor Analysis (EFA), however given the amount of detail, this is presented in Appendix B. Correlations between factors developed from the process are presented in Table  1 .

This data shows strong correlations between all variables and the BSMAS, but especially for impulsive use, engaged use, and attentional functioning (recall that as an ADHD screening tool, high scores on this measure indicate increasing attentional dysfunction). Social use has the weakest correlation with the BSMAS, albeit still significant, and further shows the weakest correlation with most other variables. To gain an initial sense for modelling these variables, a hierarchical multiple regression was conducted with BSMAS as the criterion, presented in Table  2 . Notably, attention dysregulation maintains a positive predictive effect on BSMAS, while impulsive use emerges as the strongest individual predictor. The reduction in coefficient strength for attention dysregulation with the addition of usage characteristics (from β  = 0.606, p  < .001 to β  = 0.105, p  = .013) suggests a potential mediating relationship (most likely from impulsive use β  = 0.715). Engaged entertainment use (i.e., motivations to use for entertainment, information seeking, and as a way to relieve stress) does not predict BSMAS ( β = − 0.029, p  = .466), while social connection motivation shows a significant negative relationship ( β = − 0.081, p  = .011). Neither anxiety nor depression predict PSMU ( p  > .1 for both)

Post-hoc research questions

From the above data exploration, SEM is used to evaluate predicted relationships between attentional dysregulation, social media use motives, and PSMU. This evaluation is represented by the structural diagram (Fig.  1 ), with 3 guiding questions:

Does attention dysregulation (measured by ASRS) impact PSMU (measured by BSMAS), and is this relationship mediated by impulsive, engaged, and/or social-connection oriented social media use?

Given that motivations oriented to social connection were uncorrelated with attention dysregulation, and low to zero correlation with anxiety and depression, we investigate whether social motivations influence PSMU separately, and whether this influence is mediated by impulsive and/or engaged usage.

Finally, we examine whether wellbeing indicators (DASS anxiety and depression) mediate the relationship between attention function and PSMU, through either impulsive or engaged use.

Path diagram with hypothesised path structure, predicting PSMU (BSMAS) from attention dysregulation (ASRS), mediated by social media use motives (social, engaged, and impulsive), and wellbeing factors (DASS anxiety and depression).

Confirmatory factor analysis and structural equation Model

AMOS (version 28) was used to construct a Confirmatory Factor Analysis (CFA) and test the measurement model. An initial fully covaried model demonstrated good fit, with χ 2 /df = 2.456, Goodness of Fit Index (GFI) = 0.881, Tucker Lewis Index (TLI) = 0.920, Root Mean Square of Error Approximation (RMSEA) = 0.049, and Hoelter Critical Number (CN) = 271.

Reliability was strong for all factors, with Max Reliability (H) > 0.7 for each. However, some issues with construct validity were observed. Following Fornell and Larcker ( 1981 ), and Hair et al. ( 2010 ), convergent validity was problematic, with Average Variance Extracted (AVE) below the 0.5 threshold, for BSMAS (0.46), ASRS (0.38) and engaged usage motivation (0.47), while issues with discriminant validity were observed for DASS anxiety and BSMAS factors with Maximum Shared squared Variance (MSV) higher than AVE. Removing low-loading items (2 from BSMAS, 1 from engaged use, 2 from ASRS, and 1 from DASS anxiety) largely resolved these validity issues: convergent validity becoming adequate for engaged use, and very close to the 0.5 AVE threshold for BSMAS (0.499) and for ASRS (0.486), with discriminant validity issues persisting only for anxiety. However, the model derived after adjusting factors did not alter the SEM outcomes, and so we retained the original items to maintain conceptual meaning of those factors. However, due to the noted validity concerns, we urge caution with interpreting the data, and emphasise that replication with alternate measurement tools and sample groups is required.

Common Method Variance (CMV) is known to be both a measurement and design issue (Podsakoff et al. 2003 ; Spector 2006 ), especially present in cross-sectional surveys (Jordan & Troth, 2020 ). Our survey design aimed to guard against aspects of respondent bias by ensuring anonymity, using varied scale properties, and breaking the survey into sections. Data screening to remove low effort responders (i.e., long string and elapsed time) and multivariate outliers further enhanced these efforts. We confirmed no significant CMV using Lindell and Whitney’s ( 2001 ) approach, selecting a marker variable (the describing subscale of the Five Factor Mindfulness Questionnaire, Baer et al., 2006 ) according to recommendations of Simmering et al. ( 2015 ). Please see Appendix C for more detail on this process. All zero-order correlations between predictor and criterion variables remained significant after adjusting for CMV, and therefore are not likely accounted for by CMV. Given this, composite variables were imputed without adjustment.

An initial model test demonstrated poor fit (χ 2 /df = 100.087, RMSEA = 0.404, TLI = 0.256), improved by adding covariance between error terms for anxiety and depression (χ 2 /df = 6.451, RMSEA = 0.095, TLI = 0.959). This change was flagged in modification indices, and moreover these factors are highly correlated and derive from the same overall measurement tool (DASS-21). Despite this, we opted for a cleaner approach and simply deleted depression from the model. This was warranted as its only significant relationship was with attention dysregulation, and because of the implied redundancy with anxiety. The final model showed acceptable fit, albeit with some caveats: χ 2 /df = 4.708, and so is above the generally accepted threshold of 3, and RMSEA = 0.078, which is slightly high, but acceptable considering the “ p close” test indicated a good fit ( p  = .102). Other fit indices were good: TLI = 0.971, CFI = 0.994, and CN = 336. With requisite caution due to validity issues described above, the obtained path model (see Fig.  2 ) provides insight into the relationships between attention dysregulation, social media motivations, and PSMU.

Structural model predicting PSMU (BSMAS) from attention dysregulation (ASRS), mediated by social media use motives (social, engaged, and impulsive), and anxiety (DASS_A)

As shown in Table  3 , and addressing our post-hoc research questions: (1) a significant positive association was observed between attention dysregulation (measured by ASRS) and PSMU (measured by BSMAS), mediated by impulsive use (revealed by the significant indirect effect). Engaged usage characteristics did not mediate between attention and PSMU; despite a strong significant negative direct effect between attention dysregulation and engaged use, there was no association between engaged usage and PSMU in this model ( β  = 0.022, p  = .63). (2) Social-connection motivations showed a negative direct effect on PSMU, with a positive indirect/mediation effect on PSMU through impulsive use. No indirect effect was observed through engaged use. (3) Considering the role of anxiety in the model, attention dysregulation was a strong positive predictor ( β  = 0.574). While anxiety had weak negative relationship with impulsive use ( β = − 0.103, p  = .002), and positive with engaged use ( β  = 0.09, p  = .003), the only mediation effect was through impulsive use, with a significant negative indirect effect from attention to PSMU through anxiety and impulsive use.

These findings suggest that attention dysregulation predicts PSMU, mediated by impulsive social media use. In contrast, engaged usage motivations (using social media for the purpose of entertainment, or for information-seeking) do not appear to influence PSMU, either directly or as a path with attention dysregulation – however, attention dysregulation did have a direct negative effect on engaged use motives, which may indicate that users with issues regulating attention are less likely to use social media for purposes of seeking information or for fun/entertainment. Finally, the negative effect of social engagement motivations on PSMU may indicate that using social media for the purpose of social connection represents a generally productive or positive uses of the technology. That being said, there is a mediation effect from social use to PSMU through impulsive use. Finally, there was a multi-path mediation effect from attention to PSMU through anxiety and engagement. However, the effect of anxiety in the model is quite weak ( β approximately − 0.10), and so emerges due to the substantial weights in other pathways. As such, this may be a spurious effect.

Qualitative results

The second main research aim was to explore the experiences of social media use in university students who endorse indicators of attention dysregulation. Recall that the interview pool was selected based upon two criteria: (1) a clinical ASRS score of 5 or greater (which represents a positive screen for adult ADHD), and (2) a score within the highest or lowest quartiles on the BSMAS. This was intended to offer a range of PSMU experiences in the sample. Thematic analysis of interview transcripts and open-ended questionnaire responses lead to the identification of two themes and seven subthemes focused on issues of self-regulation and social media use motives. These themes and their prevalence in the sample are presented in Table  4 .

Notably, while there was a slightly lower frequency of participants in the low BSMAS group who expressed difficulties managing their social media usage (especially “getting lost in social media”), the character of experiences appeared largely similar between high and low BSMAS categories, which raises some concerns about the effectiveness of the BSMAS in discriminating problematic social media use (at least within people who experience difficulties with attentional dysregulation). To exemplify this, Table  5 contrasts the experiences of participants across the spectrum of BSMAS scores, in view of two broad summative questions reflecting on the negative impacts of social media use, and whether this is perceived as balanced by the positives

The question framing here (specifically asking participants to reflect on negative impacts) certainly has a role in generating the kind of examples seen. This was, however, prefaced by more neutral questions (e.g., “describe your engagement with social media in a typical day” and “are there aspects of your social media usage that you find especially problematic?”). Such questions did not reveal clear differences between experiences across BSMAS score categories either. One interview question (“Do you feel a strong urge to get onto social media?”) revealed consistent differences between high and low BSMAS participants (with low BSMAS participants tending to disagree, and high BSMAS in agreement). Given the general consistency across participants, it would not be productive to contrast BSMAS groups in thematic analysis presented below. Please note, individual participants are referred to with a random participant number (i.e., P1 through P24).

The “Impossible Task:” Self-Regulation of Social Media Use

Self-regulation of social media use, particularly cutting down on the amount of time spent on social media platforms was perceived by many to be very difficult: “withdrawing from social media use altogether felt like an impossible task” (P17). This difficulty in regulating social media use is explored in the following subthemes: (1) A Conscious Effort, (2) Getting Lost in Social Media, and (3) Out of Sight, Out of Mind.

A Conscious Effort

Regulating one’s usage of social media requires a level of effortful self-control that many do not believe they possess, expressed as “I don’t feel I have much of a capacity to self-regulate” (P23) and “I feel that I don’t have control over my attention or time management” (P16). Some have such difficulty, they require someone else to help with regulating their social media use, as one participant expressed: “I need someone else to kind of tell me, to just catch me out on that, because like once I’m on there and I’m scrolling, […] I’m stuck” (P7). Others describe their self-regulation as a “conscious effort” in which they focus on being “more mindful when browsing the internet and socials” (P17).

This conscious effort to regulate one’s social media use involves more than just an awareness when using social media. Many reflected on being aware whilst using social media for longer periods than intended: “I unknowingly refresh to see more even after I told myself to set a time to get off or continuously scrolling past knowing I’m wasting time” (P14). Some described setting timers as a reminder to stop using social media, but “it’s just very easy […] to just press ignore” (P7). Awareness of problematic use is not sufficient for self-regulation as “motivation” and “self-control” also play a large role: “I have no self-regulation in regards to social media, I do try to maintain usage but it usually doesn’t work” (P13).

Getting Lost in Social Media

Some participants expressed being aware of their overuse whilst on social media, yet others articulated that they “get lost in [their] usage of social media” (P21). One participant went as far as to describe it as a “trance”-like state that they need to “snap […] out of” (P14). Unconsciously checking social media out of habit and losing track of time when on social media are key aspects of mindless use. P7 expressed that:

[checking social media is] 100% an automatic thing. I would just like go to bed, lay down and just immediately go on my phone and start scrolling, and before I realise it, like four hours later, then I become conscious.

The experience of losing track of time on social media is common, with many describing similar occurrences: “Instead of replying to the one message sent, I keep scrolling through and what initially should have been a one minute interaction becomes 30 minutes of endless scrolling” (P10). Another communicated that “time seems to move quickly” (P24) when they are using social media and that this results in excessive use. Similarly, unconscious habitual checking was a typical experience among participants, as many expressed “going on social media apps without realising and mindlessly scrolling due to habit” (P16).

“Out of Sight, Out of Mind”

The act of “subconsciously just [picking] it up and […] scrolling” (P4) and being unable to “resist looking at [their] phone” (P21) when it is easily accessible is why many take an “out of sight, out of mind” (P14) approach to self-regulation of social media. One participant expressed that they are “easily distracted from university study or tasks that require large amounts of concentration” and that this is exacerbated by social media: “the fact that social media is so readily around me through my phone being on me at all times [and] my own inability to regulate my attention has caused the overuse” (P18). As social media is a readily available distraction, the key strategy of self-regulation used by participants was to reduce the accessibility of social media. For some, this strategy involved moving social media out of reach, trying to “simply just hide it and forget about it for the rest of the day” (P13).

Removing social media from their immediate area has been found to be a successful self- regulation technique, as P14 expressed:

Having my phone out of sight and out of reach from me has proved to be effective and I think it’s true the saying, “out of sight, out of mind.” Setting alarms wasn’t as effective because I would usually just snooze or just stop the alarm and keep scrolling.

Similarly, others described “planning out [their] day” and keeping busy as their strategy for “staying distracted” from social media (P8). That is, when asked about their self-regulation of social media use, some participants voiced that they are able to stop using social media when there is “something [they are] invested in,” such that they have “no urge to pick up [their] phone” when they are engaged in another effortful activity (P5).

A common self-regulation strategy described by participants was to remove their access to social media entirely. For many, this meant deleting social media applications: “I deleted a few social media apps so as to reduce the amount of time I spend on them” (P15). Others describe doing a “social media cleanse” or going on a holiday with no reception as a means of self-regulating social media use. Less drastic than this all-or-nothing method were those that muted notifications to reduce distractions or caused social media platforms to be “a little more difficult to access through using them on a laptop” instead of a smartphone as they believed “completely removing social media will somehow lead to a relapse” (P15). Reducing their access and complicating the process of checking social media is the most common strategy used to aid in self-regulation, as expressed by P11: “I have deleted most platforms of social media in order to reduce daily consumption and hidden away the apps that I do currently have to avoid using it mindlessly.”

Purposeful Social Media Use

While many reported using social media mindlessly, almost all participants also described using social media for a variety of purposes. These intentional uses of social media are explored in the following subthemes: (1) Keeping Entertained, (2) Staying Informed and Educated, (3) Getting Motivated, (4) Connecting with Others, and (5) Escaping Reality.

Keeping Entertained

Social media use as a form of entertainment was commonly reported, with many responding that “entertainment” (P16) was their primary goal when using social media. Extending beyond entertainment purposes, social media is used to fill any spare time that individuals may have: “the purpose I have for using social media is to keep me entertained when I have nothing else to do” (P9). The phrase “nothing else to do” (P2) was repeated by a number of participants who describe using social media as a way of passing time or combating boredom: “I just scroll until I find an interesting video to waste time” (P10). In contrast, social media is also used to cultivate new hobbies and “introduce […] new areas of interest” (P19).

Staying Informed and Educated

Social media is a source of information and education for many individuals: “I more so use it to educate myself and learn up on the types of things I’ve been wanting to teach myself” (P17). Notably, many participants expressed that social media was their primary method of staying “up to date with social issues” (P9) and current world events. Others described social media use as a tool that complements their university studies, as they use social media platforms such as YouTube as “a learning tool for difficult concepts that [they were] not able to understand at university” (P10). Searching for “another perspective” (P10) proves to be valuable as social media as broadening their world view, as expressed by P19:

It has also introduced me to new areas of interest, provided information and kept me informed. It reveals to us that the world is vastly more diverse, interesting and complex than my world-view.

From remaining informed about current events to educating oneself about new topics, purposeful use of social media for some includes seeking information and education.

Connecting with Others

More prevalent than seeking motivation was the purposeful use of social media to interact with friends and family. All participants voiced that a key purpose of their social media use was to connect with others: “social media is a way to connect with and keep up with loved ones, […] reconnect with friends that I thought I had lost” (P19). Particularly salient was social media as a “way to stay in contact with friends and family overseas” (P24) and “the ability to communicate with friends that I’ve not seen in years” (P10). Connecting with others, specifically to “talk to people and see what they are up to” (P11) through posted content, was found to be the core use of social media for most individuals.

Escaping Reality

Lastly, a common use of social media is as “a coping mechanism to escape and deal with a different sort of ‘reality’” (P17). For many, social media use is a means of procrastinating and distracting oneself from tasks: “I’m a really big procrastinator and social media is a way I can escape doing my work until the last minute” (P9). Social media as an escape was a common experience as many expressed using social media for the purpose of distracting themselves and delaying confronting any issues they may be experiencing. One participant poetically described social media as “providing temporary simple pleasure that has the ability to distract an individual from the world” (P12). The escapism is not limited just to avoidance of pending tasks and to “explore the lives of others instead of focusing on [one’s] own” (P10). Many individuals expressed that social media use was a means of “filling an emotional void” (P16) and seeking “temporary alleviation of […] anxieties” (P19). While some of this is evocative of problematic use, we note that this kind of motivation can still be adaptive, as a way for individuals to gain distance from an emotion or problem that could otherwise become overwhelming.

The present study investigated the relationships between attention dysregulation, social media use motives, psychological wellbeing factors (anxiety and depression) and PSMU in a sample of Australian university students. A nested mixed-methods design was used to first identify the relationship of variables through SEM analysis, and then gain insight from interview or open-ended questionnaire within a subset of participants who demonstrated indicators of attention dysregulation (meeting screen threshold for adult ADHD), recruited with a spread of high and low BSMAS scores. Our SEM principally indicated that scores on the ASRS (reflecting attentional/self-regulation) positively predict scores on the BSMAS (reflecting PSMU/SMA) through usage characteristics of procrastination and unintentional/habitual use (we categorise as impulsive social media use). There was also an indication that social-connection motivations negatively predict BSMAS scores. There were no clear effects of wellbeing factors (anxiety or depression) in the model, the only convincing associations being that attentional dysregulation predicts elevated anxiety and depression.

Prior research has demonstrated the relationship between attention dysregulation and PSMU (Boer et al., 2020 ; Ko et al., 2009 ; Reinecke et al., 2022 ) have presented a detailed conceptual analysis of the complex role of self-regulation, alongside various contextual and motivational factors. While the model generated in this study found a particularly strong effect for impulsive use, it is likely that common (self-regulation) processes drive scores on both the ASRS as well as impulsive use. Given that the impulsive use items pertain more directly to social media use behaviours, it makes sense then that the relationship with BSMAS was particularly strong. From this, we argue that the BSMAS is flawed by its lack of a defined self-regulation component, and further by its insensitivity to contextual factors that define individual subjective experiences of social media use as problematic or otherwise (however, this is more of an issue with how such measures are used in research, rather than with the tools directly). These themes were reflected particularly clearly in the qualitative data, where a common narrative expressed substantial challenges associated with self-regulating social media use, the distress of such experiences, as well as problematic use tending to be balanced by affordances of social connection.

While there are elements of the BSMAS that tap into self-regulation, most notably mood modification (“Used social media in order to forget about personal problems”) and relapse (“Tried to cut down on the use of social media without success”), these lack conceptual clarity. This is particularly prominent for mood modification . The underlying idea is that using social media for the purpose of forgetting about personal problems is maladaptive, as articulated by Andreassen ( 2015 , p.179), because “SNS addicts engage in social networking to gain control, but become controlled by their social networks.” There is an internal logic to this assumption, reinforced by the cluster of behaviours otherwise reflected in the addiction framework (uncontrollable urges, failure to restrict use, and conflict), which may look a lot like addiction (Anderson & Wood, n.d. ). However, whether mood modification behaviours are adaptive or maladaptive will depend largely on the context in which they occur, potentially providing necessary relief from unavoidable or unchangeable internal or external stimuli, as well as access to information and social connection. While there would surely be some contexts in which the BSMAS (and other measures of PSMU/SMA) validly indicate a problematic relationship with social media, it is absolutely necessary to view such measures as broad screening tools rather than concrete measures of a particular phenomenon – especially when used in the context of broad survey studies of the general population where such screening measures are arguably prone to false positives (Maraz et al., 2015 ). This critique naturally applies also to our use of the ASRS, and despite clear indications that participants selected from it displayed indications of difficulties with self-regulation, it is nevertheless a limitation in our research. That being said, we are not endeavouring to promote claims for associations between ADHD and SMA.

More critically, researchers have questioned the prevalence and severity of social media addiction (Carbonell & Panova, 2017 ), and the concept of addiction in other areas of technology use (Aarseth et al., 2017 ; Ellis, 2019 ; Przybylski et al., 2017 ). This has led to a growing acknowledgement that the construct lacks a firm grounding, with Carbonell & Panova (p.48) cautioning that “although similarities between excessive use of SNS’ and addiction may exist, the pathologizing of the new computer-mediated form of communication needs to be met with a cautious and critical eye”. They reason that the context of use makes all the difference in terms of understanding social media use, and behaviours that may be captured in a measure of social media addiction (like conflict, salience, and mood modification) may be better understood in terms of normal psychological or developmental processes. For instance, a young person may prioritise engagement with social media over attending to classwork due to that action furthering their social capital development, and not because they are experiencing or at risk of social media addiction. Other researchers, such as Billieux et al. (2018) and Meier ( 2022 ) similarly argue that focus must shift from studying “problematic use”, to studying specific problems and processes that are most meaningful to users of social media.

One such meaningful problem is how self-control strategies can be best employed, in what contexts, to manage various aspects of behaviour related to social media use. For example, Meier (2020) reported a diary study investigating the link between mobile checking habits and procrastination, framing the research as investigating a “key functional problem” by “predicting problematic outcome (i.e., procrastination) through key aspects of person- and day-level mobile connectivity (i.e., checking habits)—rather than assuming mobile media to be problematic per se” (Meier, p.273).

In relation to this topic, our thematic analysis found that awareness of problematic use and intentions to reduce or halt social media use are not sufficient to lead to self-regulation – indeed, self-regulation of social media use was unsurprisingly perceived as effortful and difficult. The individuals who participated in our interview research reported some success when endeavouring to manage social media use by disrupting their access to social media through situational self-control strategies. Typically, this was done by (temporarily) uninstalling social media applications, deactivating social media accounts, or placing the device used to access social media in a different location. Such strategies are reportedly more effective than reactive or sheer willpower approaches, but simultaneously require more intentional effort (Brevers & Turel, 2019 ). Indeed, self-regulation strategies were generally not met with lasting success in terms of an ongoing sense of effective management of social media use, likely driven by participants’ underlying difficulties with self-regulation. Further research is needed to understand the processes involved, and to explore methods that best support individuals (especially those most prone to self-regulatory failures) in managing their social media use.

Research Limitations

We have discussed some of these core limitations above, but it is worth reiterating that there are major limitations within the PSMU/SMA field broadly, and that these have implications for the present research. Carbonell and Panova ( 2017 ), for instance, argue that addiction is a flawed model for framing social media use behaviours, as there is little compelling research identifying the addiction construct, or the severity of effects. They also argue that there is poor alignment between given measures of addiction and the behaviours or experiences they are purported to measure. This is echoed in debates within the areas of internet gaming (Aarseth et al., 2017 ; Przybylski et al., 2017 ) and smartphone use fields (Ellis, 2019 ; Griffiths et al., 2017 ) respond that gaming addiction (which would extend to other technology use problems) is a syndrome, characterized by a set of associated symptoms that tend to occur under specific circumstances, and therefore resist consistent description and symptomatology. While we should be hesitant about applying the terminology of addiction (or even softer phrasing such as “addiction-like” ) , we agree with Griffiths et al. that individuals nonetheless experience substantial distress in relation to their technology use. Indeed, from the qualitative data of the present study, it was clear that individuals do experience substantial distress in relation to their social media use. However, much more care is needed in appropriately framing social media research, and the conclusions drawn therefrom.

We also note that despite strengths in the nested mixed-methods design, the research is limited as participants were drawn from a pool of first-year university psychology students. The sample is meaningfully representative of the kinds of people who struggle with social media usage, but does not necessarily generalise beyond the context of young people who attend university. It is also noteworthy that in both quantitative and qualitative samples, 80% of participants were women. Finally, it must be noted that the COVID-19 pandemic may have influenced experiences around social media use in the participants of this research. During data collection, Sydney experienced a drastic increase in COVID-19 cases and was placed into a lockdown. A number of participants mentioned the impact of COVID-19 and isolating within homes in their responses in interviews and open-ended questionnaires, stating that social media use was increasingly important for entertainment and connection as they had more individual time and that it was one of few methods of staying in contact with others.

Conclusions

We agree that social media, as with use of many other new technologies, is overpathologized (Billieux et al., 2015 ). However, many people do experience their SMU as problematic, complex, and difficult to manage. We believe that our findings offer some useful points for further development. First, the BSMAS may be improved by the addition of an explicit self-regulation component. Second, it requires further evaluation in the context of capacities for attentional functioning. The interview data demonstrated that individuals selected for attentional dysregulation describe very similar kinds of experiences with SMU, despite being split into groups that should differ in the intensity with which they experience their use of social media as problematic. This either suggests that the BSMAS is not very good at distinguishing such experiences in general, or that it lacks effectiveness in the case of people with attentional dysregulation. Thirdly, the identification of a mediating role of impulsive use within the problematic use construct contributes to understanding how it develops, and may help explain why the interview participants express generally problematic experiences with social media use regardless of score on the BSMAS. Finally, the interview data provides a rich understanding of how individuals exhibiting attentional dysregulation manage (and struggle with) their social media usage, indicating that situational strategies may be particularly useful for this goal, yet are difficult to enact, especially for individuals with low trait self-control.

While the present research was developed from within a PSMU framework, we nevertheless hope that it contributes to the growing voices urging care with the way social media research is formulated and discussed. Important questions remain that are deserving of ongoing research efforts, and which need to be appropriately contextualised in the behaviours, processes, and outcomes that matter.

Data Availability

The datasets generated and analysed in the current study are stored in perpetuity according to the Western Sydney University Human Research Ethics Committee Extended Consent guidelines, meaning that data is available for use in related research that meets requirements of ethics review. Researchers who wish to access the data may do so by contacting the lead author, or the WSU HREC if the author is uncontactable (HREC approval number H14268).

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David Caelum Arness & Theodora Ollis

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Both authors contributed to the study conception and design. Material preparation was shared by the authors, data collection, and qualitative analysis were performed by Theodora Ollis. First draft of the manuscript was written by Theodora Ollis, with major revision by David Arness, contributing the SEM and theoretical discussion. Both authors have read and approve the final manuscript.

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Open-ended questionnaire/interview protocol

(all items presented in Qualtrics, with free response text boxes)

Please describe (briefly) your engagement with social media in a typical day.

Consider the following (you don’t need to answer all questions, they are listed here as prompts for you to respond if they resonate with you):

Do you use social media for set times, randomly, when bored?

Do you usually have a goal/purpose for using social media?

Do you lose track of time when on social media?

Any other/different points you feel are relevant?

Are there aspects of your social media usage that you find especially problematic?

This could include things like excessive use, frequently losing track of time, impacting your work or study, causing stress, anxiety etc.)

Please describe what this problematic use is like for you:

Do you often feel a strong urge to get onto social media (if yes, how does this impact your day-to-day life)?

Do you often use social media as a distraction, or to forget about difficult emotions or problems (if yes, how does this impact your day-to-day life)?

Please describe the negative impacts you experience from using social media (if any):

If relevant, do you feel that any negative impacts are balanced by the positives of social media for you (please describe)?

A key aim of this research is to explore how individual capacity for “self-regulation” might impact their experiences around social media usage (specifically, aspects of usage that they feel have a negative impact).

In answering the next set of questions, consider the following definition:

Attentional regulation is the capacity to choose where and how we focus our attention, which enables us to plan and carry out our day-to-day activities and manage distractions….

Do you feel that your capacity for self-regulation has some impact on the way you use social media (please describe)?

Do you experience specific areas of difficulty with managing social media use? Consider, for example, issues with the following:

Managing impulsive thoughts/behaviours?

Managing excessive use?

Halting social media once you’ve started?

Repetitive refresh/endless scrolling?

Anything else you’d like to describe?

Do you feel that it is necessary for you to cut down on your social media use (if yes, please describe why)?

Have you previously attempted to cut down on your social media use?

Please describe some of the methods you’ve used to attempt this?

How successful were these attempts (can you identify why these attempts were, or were not, successful)?

Can you describe how you felt/feel when restricting access to social media?

Exploratory Factor Analysis

An initial eight factors were extracted using Maximum Likelihood method, based on Eigenvalue > 1, with Promax rotation (Kappa = 4), accounting for a cumulative variance of 54.5%. The data showed good factorisability, with KMO = 0.943, and majority of extraction communalities > 0.5 (and very few < 0.3). The resulting pattern matrix somewhat confirmed expectations, but with notable exceptions.

Firstly, DASS stress scale items loaded on a single factor with DASS anxiety items (perhaps reflecting general reactivity), with much weaker loadings for the stress scale items. Second, DASS depression scale item 2 did not behave as expected, instead loading with ASRS items 1–4. This is understandable as this item assesses capacity to initiate intentional behaviour (i.e., “I found it difficult to work up the initiative to do things”), thus overlapping with the ASRS executive functioning construct. Third, items 5 and 6 of the ASRS exhibited poor loadings (low values, and in separate factors), likely because these items relate to the behavioural component of ADHD (e.g., “feeling compelled to do things, like you were driven by a motor”), compared with items 1–4, which relate to executive functioning component (e.g., “How often do you have difficulty getting things in order when you have a task that requires organisation?”).

Regarding the social media motivation items, entertainment (e.g., “I use social media because it is fun”) and information seeking (“I use social media to find information”) loaded as a factor together, along with one stress relief motivation item (“I use social media to recover from work or other exhausting tasks”), perhaps indicating a general purposeful or engaged usage orientation. This was distinct from social-connection motivations (“I use social media to catch up with friends”), which loaded together on a separate factor. Also distinct were items indicating usage that is characterised as automatic or habitual, and for procrastination (“I check my social media without thinking about it”, “I use social media even though I have more important things to do”), which loaded on a factor together. This is taken to indicate a general impulsive or reduced control component.

Lastly, it is notable that BSMAS item 3 (mood modification; “used social media in order to forget about personal problems”) had lower loading (0.479) in comparison with the rest of the BSMAS (next lowest loading = 0.615). This item is problematic as it conceptually overlaps with the DASS stress scale, and stress relief motivations (i.e., all related to processes of mood modification). It is also a problem item because using social media to shift one’s mood, by distracting oneself from personal problems, can be a rational, adaptive, and even constructive coping response and so may not well capture the construct.

As such, a second run (removing BSMAS item 3, DASS stress scale, DASS depression scale item 2, and ASRS items 5 and 6) resulted in seven factors extracted, accounting for a cumulative variance of 55.8% and sufficient factorisability (KMO = 0.921). The pattern matrix (see Table B.1) confirms factor structure for adjusted BSMAS, DASS anxiety and depression, and somewhat for ASRS as well (albeit loadings are < 0.70). The usage motivation groupings identified previously continue to hold, however with some loading issues. Specifically, one stress usage item has low loading at 0.302, and so was removed; stress item 2 displays some cross-loading with the impulsive/control factor, but as this cross-loading is very close to 0.3 the item was retained as part of the purposeful usage factor; information item 2 has lower loading at 0.355, and so was also deleted). It is noteworthy that impulsive social media usage (i.e., habitual use and procrastination) emerged as a distinct independent factor, suggesting that this reflects a construct distinct from that represented by the BSMAS.

A marker variable for screening CMV comprised items from the ‘describing’ sub-scale of the Five Factor Mindfulness Questionnaire (Baer et al., 2006 ), which refers to the ability to label internal experiences with words (participants completed the FFMQ as part of their participation in a broader study). Whilst the attention and emotion regulation components of mindfulness do overlap with study variables, and there may be some relationship between psychological wellbeing and the ability to verbalise experiences, the theoretical overlap is more obscure, and does not clearly relate to social media usage or with the normal bounds of attentional function. Nevertheless, in an effort to make the marker variable more neutral, we removed items from the describing scale that relate to emotional content (keeping, for example, “I can easily put my beliefs, opinions, and expectations into words” and removing “Even when I’m feeling terribly upset, I can find a way to put it into words”). Following recommendations of Simmering et al. ( 2015 ), this is a reasonable marker as it has minimal theoretical overlap with the other variables in the model, while the methods of data collection (Likert-type scales completed in the same testing session) should elicit similar response processes and tendencies. Further CFA results showed generally low associations between the marker and all study variables, with highest weightings around − 0.3 for attention and depression, otherwise generally much lower than other weightings in the model.

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Arness, D.C., Ollis, T. A mixed-methods study of problematic social media use, attention dysregulation, and social media use motives. Curr Psychol 42 , 24379–24398 (2023). https://doi.org/10.1007/s12144-022-03472-6

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DOI : https://doi.org/10.1007/s12144-022-03472-6

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Social media statistics: 60 key insights into social media.

This article will present 60 insightful social media statistics covering different aspects, such as growth, usage, platforms, trends, impact, and future. These statistics will help you gain a deeper and broader perspective on the topic and make informed choices for your personal or professional goals.

Social media has become an integral part of our lives. It connects us with people, information, and entertainment across the globe. It also influences our behavior, opinions, and decisions in various ways. Whether you are a casual user, a business owner, or a marketer, you need to understand the power and potential of social media in today’s digital world.

Understanding the Power of Social Media

Let’s start by looking at some statistics that show how big and influential social media is in the global context.

• There are 5.04 billion social media users around the world in January 2024 , equating to 62.3 percent of the total global population.
• Social media user numbers have continued to grow over the past 12 months too, with 266 million new users joining social media since this time last year.
• That equates to annualized growth of 5.6 percent, at an average rate of 8.4 new users every single second.
• Notably, 94.2% of internet users now have social media identities, highlighting the integral role of social media in digital communication and interaction today .
• The average daily time spent using social media is 2 hours and 23 minutes.
• Although the average daily time spent on social has slightly decreased (4 minutes), adoption growth remains high. Currently, 94.2% of internet users are using social media, and the gender split is fairly balanced, with slightly more male identities than female identities.
• Platform-wise, YouTube and WhatsApp have shown remarkable user numbers, with YouTube’s potential advertising reach hitting 2.491 billion and WhatsApp maintaining at least 2 billion monthly active users.
• Instagram also boasts 2 billion monthly active users, while TikTok’s ads can potentially reach 1.562 billion adults monthly. Other notable platforms include WeChat (including Weixin), with 1.336 billion users; Facebook Messenger, with 979 million potential advertising reach; and Telegram, with 800 million users.

These statistics demonstrate that social media is a powerful and pervasive force that affects billions of people across the world. It is not a passing trend, but a fundamental shift in the way we communicate, consume, and create information.

Social Media Growth

Now that we have established the importance and influence of social media let’s look at some statistics that show how fast and diverse social media is growing in terms of users, platforms, and content.

Insights from Global Social Media Statistics

Social media is a global phenomenon that transcends geographical, cultural, and linguistic boundaries. Here are some statistics that illustrate the diversity and dynamism of social media users and platforms around the world.

• In terms of regional social media use, 2024 data indicates varied penetration rates across different parts of the world, with Eastern Asia showing a 74% penetration rate, North America at 71%, Southern America at 66%, and both Northern and Western Europe between 80-82%. The lowest penetration rates are observed in Southern Asia (32%), Western Africa (16%), and Middle Africa (10%), illustrating the diverse adoption and usage patterns of social media globally.
• Facebook is the biggest existing social network with 2.96 billion monthly active users recorded in early 2023.
• Over 75% of the world’s population aged 13+ uses social media.
• Over 93% of internet users are social media users.
• 76% of North Americans use social media
• A recent in-depth study of social media penetration rates by country and territory shows that the United Arab Emirates currently tops the list. It has a rate of 105.6% , which suggests there are more social media users than people there.
• Most top-ranked social networks with more than 100 million user s originated in the United States, but services like Chinese social networks WeChat, QQ, or video sharing app Douyin have also garnered mainstream appeal in their respective regions due to local context and
• The country with the lowest social media penetration rate is North Korea, with 0% of its population using social media.
• The main reasons people use social media is to stay in touch with friends and family, fill their spare time, and read the news

These statistics show that social media is a diverse and dynamic landscape that offers different opportunities and challenges for users, marketers, and businesses. It is important to understand the preferences and behaviors of different audiences and regions, and to adapt to the changing trends and demands of the social media environment.

Social Media Users Worldwide

One of the key factors that drives the growth and innovation of social media is the number of users. Here are some statistics that show how many people use social media around the world, and how this number is expected to change in the future.

• A typical social media user interacts with 6.6 social media platforms .
• China, with 1.02 billion users, is the country with the most social media users. India and the United States make it to the top three with 755.47 million and 302.25 million users, respectively.
• An average person uses social media for two hours and thirty-five minutes every day.
• The average number of social media accounts used by millennial or Gen Z users worldwide is 8.5.

These statistics indicate that social media is still growing at a rapid pace, and that more and more people are joining the social media community. This means that social media will continue to offer new and exciting opportunities for communication, information, and entertainment for users, as well as for marketing, branding, and customer service for businesses.

Active Social Media Users by Region

Another important factor that influences the growth and diversity of social media is the regional distribution of users. Here are some statistics showing how many active social media users there are in different regions of the world, and how this number is expected to change.

• The region with the highest number of active social media users in 2024 is projected to be East Asia, with 1.42 billion users, which is 28.8% of the global total. The region with the lowest number of active social media users in 2024 is projected to be Oceania, with 50 million users, which is 1% of the global total.
• The region with the highest social media penetration rate in 2024 is projected to be Northern America, with 81.7% of its population using social media. The region with the lowest social media penetration rate in 2024 is projected to be Central Africa, with 10.1% of its population using social media.
• The most popular global social media platforms in 2024 are expected to be Facebook, YouTube, WhatsApp, Instagram, and TikTok, with more than one billion monthly active users each.

These statistics show that social media is not evenly distributed across the world and that there are significant differences in the number and proportion

Social Media Use Statistics

Before you can determine the best way to reach your audience through social media, you should understand how people generally use these platforms. The following statistics provide a glimpse into social media usage among various markets around the world.

Frequency of Social Media Use

• Users spend an average of 2 hours and 29 minutes daily on social media platforms.
• The countries with the highest daily social media usage in 2024 are projected to be the Philippines, Brazil, and Colombia, with more than 3 hours and 30 minutes each.
• The countries with the lowest daily social media usage in 2024 are projected to be Japan, China, and South Korea, with less than 1 hour and 30 minutes each1.
• Americans check their social media on average for 2 hours and 14 minutes per day. This is 10 minutes less than the global average.
• At the same time, Canadians spend 105 minutes every day on social media platforms.

Types of Content Consumed on Social Media

• The most effective type of content on social media in 2024 is short-form video, which has an average engagement rate of 9.3% across all platforms. Short-form video is especially popular on TikTok, YouTube Shorts, and Instagram Reels, where users can easily create and consume bite-sized content that is entertaining, informative, or educational. TikTok statistics and popularity have made this category especially popular in recent years.
• The second most effective type of content on social media in 2024 is audio chat and live rooms, which have an average engagement rate of 7.8% across all platforms. Audio chat and live rooms are interactive and immersive formats that allow users to join live conversations with hosts, guests, and other listeners. Platforms such as X Spaces, Clubhouse, and Meta Live Audio Rooms have seen a surge in popularity and usage in 2023 and 2024.
• The third most effective type of content on social media in 2024 is content that represents brand values, which has an average engagement rate of 6.5% across all platforms. Content that represents brand values is content that showcases the mission, vision, and purpose of a brand, as well as its social and environmental impact. Users are more likely to trust and support brands that align with their values and beliefs, and that demonstrate authenticity and transparency.
• The least effective type of content on social media in 2024 is high-quality images, which have an average engagement rate of 2.1% across all platforms. High-quality images are still important for visual appeal and brand recognition, but they are not enough to capture and retain users’ attention in a crowded and competitive social media landscape. Users are looking for more dynamic and engaging content that offers value, emotion, or connection.

Social Media Usage and Age Demographics

• Social media usage by age can vary widely from generation to generation. The average number of social media accounts used by millennial or Gen Z users worldwide is 8.5.
• According to research on 46 nations with internet users aged between 16 and 64, Japan had the lowest overall number of social media accounts at 3.8, while India had the highest at 11.5 accounts per user.

Social Media Use and Mobile Devices

• The number of unique mobile phone users sits at 5.61 billion at the start of 2024. The latest data from GSMA Intelligence reveals that 69.4 percent of the world’s total population now uses a mobile device, with the global total up by 138 million (+2.5 percent) since early 2023.
• 98.3% of users access social platforms via mobile .

Understanding Different Social Media Platforms

• Facebook is the biggest and most widely used social media platform in the world, with 3.05 billion monthly active users as of January 2024.
• YouTube is the second biggest and most popular social media platform in the world, with 2.5 billion monthly active users as of January 2024.
• WhatsApp is the third biggest and most widely used social media platform in the world, with 2 billion monthly active users as of January 2024.
• Instagram statistics indicate that it is the fourth biggest and most popular social media platform in the world, with 2 billion monthly active users as of January 2024.
• TikTok is the fifth biggest and fastest-growing social media platform in the world, with 1.56 billion monthly active users as of January 2024.

Social Media Usage Trends

Some of the possible factors that influence the popularity and growth of social media platforms are:

• The quality and variety of the content and features that they offer, can attract and retain users’ attention and engagement.
• The relevance and personalization of the content and features that they provide, can cater to users’ needs and preferences.
• The accessibility and affordability of the platforms can enable users to use them easily and conveniently.
• The network and community effects of the platforms can encourage users to join and invite others to join.
• The innovation and adaptation of the platforms can help them respond to changing user behaviors and market trends.

Time Spent on Social Media

• The regions with the highest daily social media usage in 2024 are projected to be South America and Africa , with more than 3 hours each.
• The regions with the lowest daily social media usage in 2024 are projected to be Europe and North America, with less than 2 hours each.
• The most popular time of day to use social media globally is 9 am, followed by 12 pm and 5 pm.
• The platform with the highest daily social media usage in 2024 is TikTok, with an average of 52 minutes per user per day, followed by Instagram with 49 minutes, and Facebook with 38 minutes.
• The platform with the lowest daily social media usage in 2024 is Twitter, with an average of 10 minutes per user per day, followed by Pinterest with 14 minutes, and LinkedIn with 17 minutes.
• The platform with the highest monthly social media usage in 2024 is Facebook, with an average of 19.5 hours per user per month, followed by YouTube with 18.8 hours, and Instagram with 14.8 hours.

The Impact of Social Media on Society

Social media now plays a crucial role in modern society, impacting communication, information consumption, and business, among other areas of our lives. Social media platforms facilitate cross-border user interaction across political, cultural, and geographic divides by allowing users to produce, share, and consume information.

Social Media and Communication

Social media has transformed global communication by increasing accessibility, affordability, diversity, inclusivity, and collaboration. It allows users to communicate with anyone, anywhere, anytime, using various modes and reducing costs, especially for remote or underdeveloped areas.

Social Media and Information Consumption

Social media has significantly impacted information consumption by increasing accessibility, personalizing information, and facilitating engagement. It offers vast information on various topics, allowing users to access diverse sources and perspectives. Social media platforms also enable users to engage in information production and distribution, allowing them to like, comment, share, and rate content. This has led to a more diverse and engaging society.

The Influence of Social Media on Business

A significant impact of social media is its influence on business. Social media has become a powerful tool for businesses to market their products and services, as well as to engage with their customers.

Social Media Marketing

Social media marketing is the use of social media platforms to promote a brand, product, or service, as well as to achieve various business goals, such as increasing awareness, traffic, leads, sales, and loyalty. Social media marketing involves creating and sharing content, such as posts, stories, videos, and ads, that are relevant, valuable, and engaging to the target audience.

Social Media and Customer Engagement

Social media and customer engagement is the use of social media platforms to interact with customers, as well as to build and maintain relationships with them. Social media and customer engagement involve listening and responding to customer feedback, questions, and complaints, as well as providing customer support, service, and satisfaction.

Future Trends in Social Media Networks

Social media networks are constantly evolving to meet user needs and preferences, enabling innovation, collaboration, and influence. Future trends in social media networks include advancements in technology, content, user behavior, and business. Technology will see the integration of augmented reality (AR) and virtual reality (VR), artificial intelligence (AI) and machine learning (ML), blockchain and cryptocurrency, and the rise of short-form and bite-sized content. Content will also evolve, with short-form content dominating, audio and live content becoming more popular, and content representing brand values becoming more influential. User behavior will change, with search becoming more common and frequent, and search will become more personalized and contextualized. These trends will shape the social media landscape in 2024 and beyond, allowing users to explore new possibilities, engage with brands, and participate in the social media economy.

Emerging Social Media Platforms

• Threads, the explosive sensation of 2023 , has disrupted the social media world with an impressive user base of 131 million.
• Clubhouse, the audio-chat social app, has over 28 million downloads worldwide. Always at the forefront of social interaction, it offers innovative features like chat moderation, rooms, events, and exclusive clubs.
• Anchor, now occupied by Spotify, is one of the most essential apps for podcast enthusiasts. Designed for both smartphone and web users, it unlocks a wealth of editing possibilities with a single tap on the record icon, ensuring your podcast stands out whenever creativity strikes.
• Mastodon had 2.5 million monthly users by December 2022. Solidifying its position as an open-source powerhouse, it provides seamless sharing of text, images, and videos to its users.
• Patreon allows creators to build a direct relationship with their fans and supporters. Creators can offer exclusive content such as early access to new episodes, behind-the-scenes footage, and merchandise discounts. Fans can choose to support their favorite creators on a monthly basis.
• The popularity of Patreon has lured creators to join the platform in order to reach new audiences. In the last 3 years, more than 50,000 creators have joined the platform.

Predicted Trends in Social Media Use

• By 2025, advertisers are expected to spend over 82 billion dollars promoting their products on social networks.
• Ad spending is expected to show an annual growth rate (CAGR 2024-2028) of 6.04%, resulting in a projected market volume of US$241.9bn by 2028.
• With a projected market volume of US$84,610m in 2024, most revenue will be generated in the United States.
• 58. Ad spending in the Video Advertising market is projected to reach US$191.3bn in 2024.
• Ad spending on short-form videos in the Video Advertising market is projected to reach US$99.4bn in 2024.
• Ad spending on short-form videos is expected to show an annual growth rate (CAGR 2024-2028) of 10.04%, resulting in a projected market volume of US$145.8bn by 2028.

FAQs: Social Media Statistics

What percent of the world uses social media.

According to 2024 data, 62.3% of the global population, equivalent to 5.04 billion people, actively use social media, underscoring the expansive reach and influence of social platforms worldwide.

What is the number 1 social media platform in the world?

As of early 2023, Facebook statistics indicate that it remains the dominant social media platform globally, boasting 2.96 billion monthly active users, making it a cornerstone in both social networking and social media marketing strategies.

What are 5 facts about social media?

• The global average time spent on social media is approximately 2 hours and 23 minutes per day.
• Leading platforms by user count include YouTube and WhatsApp, with YouTube reaching a potential advertising audience of 2.491 billion and WhatsApp maintaining 2 billion monthly active users.
• Social media usage statistics reveal that over 75% of the global population aged 13 and above engages with social media.
• The diversity in social media usage across regions is significant, from Eastern Asia’s 74% penetration to Western Africa’s 16%.
• Users typically interact with an average of 6.6 different social media platforms, highlighting the importance of a multi-platform social media marketing strategy.

What are the most popular social media platforms worldwide?

The major social media platforms, each with over a billion monthly active users, include Facebook, YouTube, WhatsApp, Instagram, and TikTok, indicating their critical roles in global communication and social media marketing.

How much time do people spend on social media daily?

On average, daily social media usage stands at 2 hours and 29 minutes, illustrating the significant role of social media in people’s daily routines and its potential for targeted social media advertising.

How has social media changed the way businesses interact with customers?

Social media has revolutionized the approach to customer engagement, allowing businesses to leverage social media advertising and marketing strategies for direct communication, real-time feedback, and personalized marketing, enhancing overall customer experience and brand loyalty.

What is the future of social media?

Future trends in social media point towards the integration of advanced technologies like AR/VR and AI, an emphasis on short-form content, and evolving social media marketing strategies aimed at fostering more immersive and personalized user experiences.

How does social media usage vary across different regions?

Social media usage statistics reveal significant regional variances, with penetration rates ranging from Eastern Asia’s high of 74% to Western Africa’s low of 16%, indicating the need for region-specific social media strategies to effectively reach diverse audiences.

Where can I find the Latest social media statistics?

For the most current social media statistics, including usage, advertising, and marketing trends, professionals and businesses should consult authoritative industry reports, digital marketing research publications, and updates directly from major social media platforms. These resources offer valuable insights into social media stats that can inform effective social media strategies and advertising campaigns. You can also search for statistics that relate to specific age groups or platforms. For example, you might search “Snapchat statistics,” “Twitter statistics,” or “LinkedIn statistics.”

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How Marketers Choose College Athlete Influencers

• Kimberly A. Whitler
• Graham Twente

The authors’ research findings: Athletes’ image and quality of social media posts are more important than their follower counts, posts should feature sports more than personal content, and sexy imagery should be avoided.

Here are the characteristics that matter most.

Since 2021 McDonald’s, Microsoft, PepsiCo, Berkshire Hathaway, Amazon, Unilever, and other leading companies have done something that was never before possible: They have paid U.S. college athletes to act as product endorsers and influencers. Until a Supreme Court ruling that year, paying college athletes was forbidden under the rules of the National Collegiate Athletic Association (NCAA). In the aftermath of the Court’s ruling, the NCAA adopted a policy that enabled more than 520,000 student athletes to monetize their names, images, and likenesses by signing what have become known as NIL deals. Although no definitive count exists of athletes who have signed such deals, 278 students (40% of varsity athletes) at Texas Tech had been sponsored as of 2022. In just a few years marketers have already spent more than $1 billion on such endorsements. For individual athletes these deals can be lucrative. Consider Paige Bueckers, a University of Connecticut basketball player, whom Gatorade chose as its first sponsored college athlete. Bueckers is expected to earn more than $1 million while playing college basketball.

Over the past 20 years the social media influencer industry has completely rearranged the way information and culture are conceived, produced, marketed, and shared. This month’s Spotlight package looks at how brands are responding.

• Kimberly A. Whitler is the Frank M. Sands Sr. Associate Professor of Business Administration at the University of Virginia’s Darden School of Business and a coauthor of Athlete Brands: How to Benefit from Your Name, Image & Likeness .
• GT Graham Twente is a former senior research manager at the Darden School of Business and currently a member of the data and analytics technology consulting staff at EY in Washington, DC.

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Effects of restricting social media usage on wellbeing and performance: A randomized control trial among students

Avinash Collis

1 McCombs School of Business, The University of Texas, Austin, Texas, United States of America

Felix Eggers

2 Copenhagen Business School, Frederiksberg, Copenhagen, Denmark

Associated Data

Raw data cannot be shared publicly since it is protected under GDPR. The data was collected at the University of Groningen and contains sensitive data including all online activities of students and their grades. The IRB of the University of Groningen did not give the authors permission to share raw data. Please contact ln.gur@ycavirp if you have any questions regarding this. If you receive approval from the privacy office at the University of Groningen ( ln.gur@ycavirp ), we will share the raw data with you. Minimal data used to replicate the study's main results are provided at: https://osf.io/jfqp3/ (DOI 10.17605/OSF.IO/JFQP3 ).

Recent research has shown that social media services create large consumer surplus. Despite their positive impact on economic welfare, concerns are raised about the negative association between social media usage and well-being or performance. However, causal empirical evidence is still scarce. To address this research gap, we conduct a randomized controlled trial among students in which we track participants’ daily digital activities over the course of three quarters of an academic year. In the experiment, we randomly allocate half of the sample to a treatment condition in which social media usage (Facebook, Instagram, and Snapchat) is restricted to a maximum of 10 minutes per day. We find that participants in the treatment group substitute social media for instant messaging and do not decrease their total time spent on digital devices. Contrary to findings from previous correlational studies, we do not find any significant impact of social media usage as it was defined in our study on well-being and academic success. Our results also suggest that antitrust authorities should consider instant messaging and social media services as direct competitors before approving acquisitions.

Introduction

Social media increasingly plays an important role in our daily lives. Ever since the launch of major modern social media platforms such as Facebook, users have adopted them at an explosive pace and adoption continues to increase to this day. Almost 3 billion users are active monthly on Facebook in 2021 [ 1 ]. This corresponds to over a third of the global population. The average adult spends over 45 minutes every day on social media platforms [ 2 ].

Given this rapid adoption and usage of social media platforms, it is essential to study the impact of social media on the well-being of users. Brynjolfsson et al. [ 3 ] find that digital technologies, including social media, generate a large amount of consumer surplus. More specifically, they conduct incentive compatible choice experiments to measure the consumer surplus generated by Facebook and find that the median US Facebook user obtains around $48/month of value from using Facebook in 2017 as measured from their willingness to accept to give up access to Facebook for a month. They also conduct a similar experiment with students at a large European university and find that the median student in their sample obtains €97/month of value from using Facebook.

While Facebook and other social media services seem to generate a large amount of consumer surplus and contribute towards the economic well-being of their users, questions are raised about the negative externalities generated by social media. There is an active debate in media and academic research about the impact of social media on subjective well-being (including happiness and life satisfaction) and productivity. Current empirical results are ambiguous. Across different studies, correlational evidence points towards a positive, neutral (null results) and negative relationship between social media use and well-being (see Haidt [ 4 ] for a comprehensive literature review of social media use and mental health). However, most of this evidence suffers from issues related to reverse causality [ 5 ] and inaccurate measures of self-reported social media use [ 6 ]. Rigorous causal evidence on long term impacts of social media use on well-being is lacking.

Concerns are also raised in the field of Education policy on the impact of screen time (including social media use) on academic performance of students. Critics contend that social media use on smartphones distracts students from focusing in classes and affects their grades. Motivated by these concerns, the French education ministry banned smartphones in schools from first through ninth grades [ 7 ]. The American Academy of Pediatrics also recommends parents to limit the time spent by children and adolescents on social media so that they have enough time left to study [ 8 ]. However, a rigorous analysis of the data used in previous correlational studies that were used as evidence to support these policies suggests that the effects of social media use and screen time on adolescent well-being are too small to warrant policy changes [ 9 ].

Given these widespread concerns and conflicting correlational evidence on the impact of social media on well-being, it is necessary to obtain causal evidence in a timely manner before policies are implemented hastily. We seek to fill this research gap by conducting a randomized controlled trial to measure the causal long-term impact of social media use on academic performance and well-being. Specifically, the research question we aim to answer is: How does restricting social media use affect a) well-being, b) academic performance, and c) other digital activities?

We recruit students at a large European university to be part of our study over the course of three academic terms (quarters). The subjects install a software (RescueTime) on their personal computers and mobile devices. This software tracks all of the digital activities of the subjects during the entire duration of the study period. The first term serves as the baseline period. In the second term, subjects are randomized into treatment and control groups and the treatment group has social media use (Facebook, Instagram and Snapchat) restricted to a maximum of 10 minutes per day across all devices. We then measure the post-treatment effects in the third term.

We observe the entire space of digital activities performed by our subjects that covers online and also offline activities on their devices, including activities related to learning (such as writing in Microsoft Word or reading a PDF). Our social media use metrics are computed based on the actual time spent on social media and are not based on self-reported metrics of time spent, which is predominantly used in the existing literature. In addition to the digital activities, we obtain objective metrics of performance (grades) and subjective well-being scores solicited through surveys.

Contrary to results from previous studies using observational data, we do not find evidence that social media causes a positive or negative impact on well-being (including life satisfaction and mental health). Moreover, we also do not find any evidence that social media usage impacts academic success. However, we find significant substitution effects. Specifically, we see that participants in the treatment group substituted their use of social media services for instant messaging apps (e.g. WhatsApp). In total, these participants do not spend less time on their digital devices (computers and mobile phones) as those in the control group.

Our paper makes three main contributions. First, we test the popular media narrative portraying social media as the villain responsible for negatively affecting well-being of society. We do not find any evidence supporting this hypothesis. Second, educators and parents are increasingly concerned about the impact of digital distractions on academic performance and are restricting the online activities of students (for example through parental control software or by taking away their devices). While previous evidence seems to suggest that device usage in class might negatively affect academic performance, our results show that restricting social media usage from the lives of students (inside and outside class) might not have the intended effect. Finally, our paper is one of the first to provide evidence of substitutability between social media and instant messaging apps. This has major implications for antitrust authorities analyzing the market power of major social media platforms such as Facebook which owns Instagram (another social media service) and WhatsApp (instant messaging service).

The paper proceeds as follows. In the next section, we provide a brief review of existing literature on the impact of social media use on well-being and academic performance. In the following section, we describe the design of our experiment and data collected over the course of the study. We then show the main results and conclude with a discussion of the limitations of this study and directions for future research.

Related literature

The impact of the internet in general, and social media in particular, on well-being has attracted the attention of a number of researchers in the fields of psychology, epidemiology and human-computer interaction (HCI) over the past two decades. Most of this literature uses self-reported metrics of technology use and provides cross-sectional correlational evidence. Kraut and Burke [ 10 ] provide a review of this literature and express skepticism regarding cross-sectional and survey-based studies due to the presence of several confounding factors. Moreover, correlational studies might suffer from an abundance of researcher degrees of freedom and the file drawer problem such that only significant results are published, inevitably leading to the implication that social media usage either has a positive or negative effect. However, a null result or insignificant findings regarding social media usage might be a plausible outcome.

Orben and Przybylski [ 9 ] rigorously analyze popular large scale social datasets (n = 350k) used in previous correlational studies studying the impact of technology use on well-being by conducting a specification curve analysis of the data. This analysis involves running all possible analytical models using various combinations of the covariates. Instead of selective reporting, results from all of these analyses are reported. They find a small negative association between digital technology use and adolescent well-being. However this effect is economically insignificant explaining at most 0.4% of the variation in well-being. For comparison, the authors show that seemingly neutral activities such as eating potatoes have the same negative association with well-being as technology use. Given these concerns with correlational analyses involving cross sectional data, Kraut and Burke [ 10 ] call for experimental evidence paired with tracking data to provide reliable evidence on the relationship between internet use and well-being.

The subset of literature focusing on the association between social media use and well-being has found a wide range of effects (negative, mixed, positive and null). Using a longitudinal survey, Shakya and Christakis [ 11 ] found a negative association between Facebook use and well-being. In contrast, Burke et al. [ 12 ] find a positive association between directed communication on Facebook and social well-being due to subjects reporting improved feelings of social bonding and reduced loneliness. Similarly, Hobbs et al. [ 13 ] match Facebook profiles with public health records and find that being more socially integrated online (by accepting more Facebook friends) is associated with reduced risk of mortality. Burke and Kraut [ 14 ] find that targeted messages from strong ties is associated with positive improvements in well-being while viewing messages from friends broadcasted to all of their friends and receiving one-click feedbacks were not associated with any improvement in well-being. Kim and Shen [ 15 ] find that directed communication activities on social media platforms are positively associated with life satisfaction for older users.

Schemer et al. [ 16 ] and Johannes et al. [ 17 ] do not find any substantial relationship between social media use and subjective well-being. Liu et al. [ 18 ] conduct a meta-analysis of 124 studies and find that the association between social media use and well-being depends on the type of social media use. This association is positive for social interactions and negative for content consumption. Huang [ 19 ] also conducted a meta-analysis of 61 studies and documented a weak correlation between time spent on social media and negative indicators including depression and loneliness. For a comprehensive list of all studies studying social media use and well-being, see literature reviews by Appel et al. [ 20 ], Meier and Reinecke [ 21 ], Dienlin and Johannes [ 22 ], Masur [ 23 ] and Orben [ 24 ]. Cheng et al. [ 5 ] combine a survey of Facebook users with their Facebook activities and find that subjects reporting problematic use of Facebook were also going through a major life event such as a breakup. This shows that confounding variables could be a major concern in previous studies associating social media use and well-being.

Orben et al. [ 6 ] use a large-scale longitudinal dataset and conduct a specification curve analysis to rigorously analyze the relationship between adolescent social media use and well-being. Most of the analyses report tiny, trivial and insignificant results. Moreover, they provide evidence for reverse causality showing that social media use predicts well-being in the future and vice versa. Similarly, Sewall et al. [ 25 ] also find little to no evidence that changes in social media or smart phone use predict psychological distress.

Another major concern related to existing studies is the use of self-reported usage data. Survey respondents are typically asked to report the average time they spend on the internet, social media and digital devices. Several papers show that self-reported measures of technology use (including social media usage) are poorly correlated with actual usage and contain systematic patterns of misreporting [ 26 – 29 ].

Given this inconclusive evidence and lack of objective technology use data in existing literature, it is essential to obtain reliable causal evidence in a timely manner to inform policy makers. We aim to resolve this gap by obtaining evidence through a randomized controlled trial and using objective technology use metrics tracked by a software installed on the digital devices of our experimental subjects. In terms of outcome variables, we track measures of subjective well-being (life satisfaction and mental health) and performance (grades and number of credit points) over the duration of three quarters of an academic year (8 months) with the actual treatment lasting 2.5 months.

There is a related stream of literature using experiments to study the relationship between social media or computer usage and well-being or performance. Verduyn et al. [ 30 ] conduct a lab experiment where subjects are primed to passively use Facebook for 10 minutes and find that passive use is associated with a decline in subjective well-being. However, it is not straightforward if results from a 10-minute treatment can be generalized to long term effects.

Marotta and Acquisti [ 31 ] conduct an experiment with workers recruited from Amazon mechanical turk and offer productivity enhancing tools to subjects. One of the treatment groups has popular social media sites blocked during work hours. They find that workers in this group completed more tasks and increased their earnings. Carter et al. [ 32 ] conduct a randomized controlled trial in a US university where classes in the treatment group prohibited the use of computers in the class. They find that average exam scores were higher in the treatment group compared to the control group classes where students were allowed to use their computers. Using causal inference methods on observational data, Belo et al. [ 33 ] and Beland and Murphy [ 34 ] study the impact of broadband access and banning mobile phones in schools respectively on academic performance and also find evidence suggesting that digital distractions during class reduce academic performance. Taken together, evidence seems to suggest that digital device use in class or at work is harmful for student or worker performance. However, the overall causal impact of social media usage in life (inside and outside class or at work) on performance and well-being still remains an open question. Our study complements this research by analyzing the overall long-term impact of social media on well-being and performance as the subjects in our treatment group has restricted use of social media throughout their day for a long period of time.

A study closely related to our research is the experiment conducted by Allcott et al. [ 35 ]. They conducted a randomized controlled trial of Facebook users where subjects in the treatment group had to deactivate their Facebook account for 1 month. They find that this treatment reduced total online activity including other social media and this reduction persists after the end of the experiment. However, they use self-reported metrics of usage of online activities which are weakly correlated to objective usage metrics according to previous research. They measure 11 different metrics of subjective well-being and find that deactivating Facebook led to increase in subjective well-being for 4 out of the 11 metrics. Other related experiments include Brailovskaia et al. [ 36 ] (two weeks intervention, self-reported Facebook usage measure), Hall et al. [ 37 ] (one to four weeks intervention, self-reported Facebook usage measure), Hinsch and Sheldon [ 38 ] (one week intervention, self-reported Facebook usage measure), Tromholt [ 39 ] (one week intervention, self-reported Facebook usage measure), Mosquera et al. [ 40 ] (one week intervention), Hanley et al. [ 41 ] (one week intervention) and Vanman et al. [ 42 ] (five days intervention). Overall, the magnitude of the effects is small and it is not clear if these effects would have persisted for a treatment of longer duration. For a longer treatment duration, subjects could learn to live in a world without Facebook by discovering alternative substitutes providing similar use cases and their subjective well-being scores could go back to pre-experiment levels.

Experimental procedure

We recruited students in the faculty of economics and business of a large European university to take part in an academic study. The study received approval from the Institutional Review Board of the university. We used a flyer to invite students in lectures and from the pool of participants of the behavioral research lab of the university. The flyer informed students about the subject of the study, the required activities, the reward, and about measures to protect the participants’ privacy. Specifically, we let the students know that the study required to install a software (RescueTime) on their computers and mobile devices that keeps track of their digital activities and that allows them to analyze how much time they are spending on various categories of activities. We also stated that the study tracks their academic performance and well-being. Moreover, we informed the students that, in order to qualify for the reward, they need to keep the software running during the time of the study and to take part in four online surveys; one at the beginning of the study and one after each quarter. In addition to getting the software for free, we offered students €20 and a one out of 100 chance to win €1,000 if they take part until the end of the study.

The sign-up link forwarded interested students to a registration form that provided a more detailed privacy statement (about aim and principles of processing personal data, the types of data used, the limited recipients of the data, the storage period, and the students’ rights), informed consent, and asked students for their student email address, basic information about their studies (program, year), and the number and type of computers and mobile devices. The registered students were then invited to the study according to the experimental design detailed below.

Experimental design

The recruitment of students took place in the first quarter of the academic year 2018/19. We scheduled the experiment to run for the remaining three academic quarters. We will refer to these three terms as block 1, 2, and 3 of the study (which are quarter 2, 3, and 4 of the academic year). Each block consists of seven weeks of teaching and two examination weeks. The specific timeline was:

• Block 1: from mid-November to end of January, with holidays from December 24 to January 3.
• Block 2: from February to mid-April.
• Block 3: from mid-April to end of June, with holidays from April 19 to 24.

We used the first block to establish a baseline of the students’ digital activities. In block 2, we randomly assigned participants to one of two conditions: a control group without specific instructions and a treatment group that received an incentive to use social media as little as possible. Specifically, we instructed them to use Facebook, Instagram and Snapchat for a maximum of 10 minutes per day. We did not block these services completely because not having access to social media at all might have a negative effect on students, e.g., if they use it to exchange important information about their studies. The 10-minute limit enables students to still access relevant information while not allowing them to waste a longer period of time. This is consistent with the Goldilocks hypothesis according to which moderate digital use may be advantageous compared to no use or overuse [ 43 ]. The software would inform students in the treatment group when they reached the limit and automatically block Facebook, Instagram and Snapchat afterwards. Students could disable this feature if they needed to use these services for longer. We informed students that we gave away another €1,000 among all students who achieved to stay under the 10-minute limit throughout block 2. Block 3 served to assess post-treatment effects.

We focus on Facebook, Instagram, and Snapchat in our experiment because they were the most popular social media services according to our measurement in block 1. As a caveat, the distinction of these services from others such as instant messaging can be debated. We return to this issue in the discussion.

We invited students to four surveys in total. We have sent the first survey in the first week of block 1. This survey asked students to give informed consent and, after referring them to the privacy statement, their agreement to use their academic grades for the purpose of the study. Moreover, we asked them about basic demographic information, their study program, and additional work activities next to their studies. Moreover, we provided measures of subjective well-being (see specific measures below). Upon completion of this first survey, we gave students the installation and registration instructions for the tracking software and asked them to keep this software running henceforth on all their computers and mobile devices. While the software was supported by Windows, OS X, and Android devices, it was not compatible with iOS devices (iPhone or iPad). In order to make sure that students with iOS devices complied to the 10 minute limit in the treatment condition, we informed them that we will ask them at a random time to hand in a screenshot of the Screen Time feature of iOS that reports similar information.

Surveys 2, 3, and 4 followed after each block and repeated the subjective well-being measures in order to track students’ well-being over time. We gave students a one-week deadline to fill out each survey.

Survey measures

As measures of subjective well-being we use the satisfaction with life scale (SWLS) [ 44 ] that consists of five items (In most ways my life is close to my ideal; The conditions of my life are excellent; I am satisfied with my life; So far I have gotten the important things I want in life; If I could live my life over, I would change almost nothing). These items are measured on a 7-point scale (1 “strongly disagree” to 7 “strongly agree”). SWLS is the most widely used scale to measure subjective well-being and is also used in previous studies studying social media use and well-being (e.g., [ 30 , 45 ]). In addition to SWLS, we also collected direct measures of happiness and life satisfaction through standard questions widely used in previous literature. Besides numerous other studies, the happiness question is used in the World values survey [ 46 ] and the life satisfaction question is used by Gallup [ 47 ] to calculate its well-being index. These questions are highly correlated with objective measures of well-being such as brain activity, emotional expressions and suicide rates as well as decision utility [ 48 ]. We obtain qualitatively similar results using these happiness and life satisfaction scores as we found using SWLS.

For measuring mental well-being, we adopted the shortened Warwick-Edinburgh Mental Well-being Scale (SWEMWBS) [ 49 , 50 ] with seven items (I’ve been feeling optimistic about the future; I’ve been feeling useful; I’ve been feeling relaxed; I’ve been dealing with problems well; I’ve been thinking clearly; I’ve been feeling close to other people; I’ve been able to make up my own mind about things). These items are assessed on a 5-point scale ranging from “None of the time” to “All of the time”. The SWEMWBS is a popular scale to measure mental well-being and is used in previous studies studying technology use and mental well-being [ 43 ].

Overview of data sources

Overall, our study makes use of three data sources: digital activities tracked by the software, self-reported measures via surveys, and academic grades from the educational administration. Table 1 shows an overview of these data types.

The software tracks users’ activities on each device in 5-minute intervals and records how many seconds a user has actively used a specific program, app, or website in this interval, ranging from 1 to 300 seconds. Specifically, it records the user id, the name of the activity, the system name (Windows, Mac OS, or Android), and a timestamp. Since we are specifically interested in social media activities of the three most used social network services Facebook, Instagram and Snapchat, we used lookup tables to classify activities accordingly. For example, Facebook usage could appear in the activities as “ facebook.com ”, “ fb.com ”, “ messenger.com ”, “Facebook for Android”, “Facebook for Windows”, etc. We gathered this list of activities in block 1 and used each of these activities to count toward the 10-minute limit for the treatment group in block 2.

The European university at which this study took place uses a grading system that ranges from 0 to 10. Any grade below 6 represents a fail. A grade of 7 is most common and often referred to as “standard”, a 6 as “below standard“, and an 8 or higher as “above standard”. The grade for a lecture typically consists of a combined grade of the final exam and assignments that have to be completed during the course.

A total of 191 respondents completed the first survey. As is typical for longitudinal studies, some students dropped out over time such that 157 students completed survey 2, 144 survey 3, and 121 the final survey. The survey participation corresponds to the number of participants who reported digital activities using the software (see Table 2 ).

The following results will be based on the sample that recorded activities for at least 30 days in block 1 and 2 and completed surveys 1, 2, and 3. We will analyze the post-treatment data from block 3 and survey 4 separately. From the 134 students who recorded activities in block 1 and 2, we were able to match 122 from all data sources, i.e., twelve students did not answer (one of) the surveys or did not follow courses in at least one of the blocks.

Despite the dropouts, most importantly, there are no significant differences between the treatment and the control group, in terms of gender (Chi-squared test, p = 0.471), age (t-test, p = 0.961), mobile device operating system (Chi-squared test, p = 1.000), number of years studying at the university (t-test, p = 0.334) or whether students are working next to their studies in block 1 (Chi-squared test, p = 0.974) or block 2 (p = 0.594) (see Table 3 for details). There are also no significant differences between those who started the study and those who dropped out in terms of gender (Chi-squared test, p = 0.701), age (t-test, p = 0.113), mobile operation system (Chi-squared test, p = 0.975), of work status in block 1 (Chi-squared test, p = 0.109) or block 2 (p = 0.169). However, there is a significant difference between these samples regarding the study year (t-test, p = 0.027) such that those who dropped out are more likely to be Bachelor degree students than Master’s students. One potential explanation is that Bachelor degree students are more likely to quit their studies and not have any courses or grades registered subsequently.

For the subsequent analysis, we note that at 80% power and alpha = 5%, the minimum detectable difference with our sample size is 2.5 for SWLS (assuming a standard deviation of 5.0), 1.5 for SWEMWBS (standard deviation = 3.0), and 0.5 for academic grades (standard deviation of 1.0).

Digital activities

Social media usage.

On average, students tracked 223.7 minutes of digital activities per day across the entire study (SD = 115.1 minutes). Students who use an Android mobile device recorded significantly more activities (265.6 minutes; t-test, p < 0.001), compared to students with an iOS device (182.5 minutes) as iOS was not supported by the software. While our activity estimates are more accurate for Android users we expect the treatment condition to be equally effective for both of these segments because we informed participants to also inspect their iOS tracked activities (see above).

Fig 1 shows the total number of minutes tracked by day, averaged for students with Android mobile devices in the treatment (black dots) and control groups (white dots). We report the activities for users with Android devices because the tracking is more accurate as it captures activities on desktop/laptop computers and their mobile devices (figures corresponding to the overall sample are in the Supporting Information, S1 Fig ). The solid vertical lines separate blocks 1, 2, and 3 and the dashed vertical lines indicate the start of the examination period. Overall, digital activities remain on a high level each day but are reduced during the winter holiday season and during the examination periods.

As a manipulation check, the bottom part of Fig 1 shows activities for social networking (Facebook, Instagram, and Snapchat combined) for the Android sample. The mean daily usage in minutes is 21.1 minutes (27.9 minutes) for users in the treatment (control) group in block 1, which is not significantly different (t-test, p = 0.310). The incentive to reduce social media activities was effective as students in the treatment condition significantly reduced their social media usage in block 2 compared to the control group (t-test, p = 0.009). The horizontal line represents the 10-minute limit imposed on the treatment group. The average usage per day is close to the limit in the treatment group with 8.1 minutes. Within the control group, the average daily usage of 24.2 minutes in block 2 is on the same level as in the first block (paired-sample t-test, p = 0.245).

Remarkably, although students in the treatment group significantly reduced their social media activities, their overall digital activities overall are not affected but, in fact, exceed those of the control group in block 2 (t-test, p = 0.026). This result indicates that students substituted or even overcompensated their social media usage with other activities.

Substitution . Fig 2A and 2B show the time series of activities of users with an Android device for the most used categories of services (we exclude the categories of general utilities, which holds mostly operating system activities, and uncategorized services for which there are no significant differences between the groups; activities for all users are in the Supporting Information, S2A and S2B Fig ). We find significant substitution for social networking with instant messaging (p = 0.008). Accordingly, more students in the treatment condition used instant messaging in block 2 when social media was restricted compared to block 1 and to the control group (difference-in-differences). The activities increased from an average daily use of 25.1 minutes in block 1 to 28.8 minutes in block 2 in the treatment group, while the usage decreased from 21.8 minutes to 15.2 minutes in the control group. Most activities (92.9%) in this category are related to WhatsApp.

a. Tracked digital activities over time (users with Android devices). b. Tracked digital activities over time (users with Android devices).

We also find a significant increase in usage of music for Android users (t-test, p = 0.027) in the treatment group in block 2. However, average daily activities in this category are rather low (below five minutes) and the difference is mostly driven by two outliers who listen to music for more than 30 minutes each day on average. Other activities show plausible patterns, e.g., the reference and learning category (activities include the university intranet, PDF reader, Wikipedia, Mendeley, Google scholar, EBSCO, etc.) shows peaks before the exam period. However, these and other activities are not affected by reduced social media usage (an overview of significance tests comparing treatment and control groups in block 2 vs. block 1 is given in the Supporting Information, S1 Table ).

Subjective well-being

For the subjective well-being measures, the SWLS and SWEMWBS items show high reliability (Cronbach’s α being 0.84 and 0.76 respectively). For the analysis we calculate scores as the sum of their items (with SWEMWBS being transformed according to a defined conversion table). The students score averages (SD) in SWLS of 25.0 (5.5), 25.0, (5.4), 25.1 (5.3) in the three surveys at the beginning of the study and after block 1 and 2. That means they are between an “average” and “high” score of satisfaction. Treatment and control group are not significantly different at the beginning of block 1 (t-test, p = 0.182; survey 1), at the end of block 1 (t-test, p = 0.212; survey 2), or, most importantly, at the end of block 2 after the exposure to the treatment (t-test, p = 0.167; survey 3). The same implications hold for the SWEMWBS scores that shows average scores (SD) of 22.8 (3.0), 22.5 (2.7), and 22.4 (3.3) in the three surveys (see S2 Table in the Supporting Information for details).

Fig 3 plots the differences between survey 3 and survey 2 (before and after the social media restriction) in terms of SWLS and SWEMBS. The distributions are centered on zero, illustrating the non-significant difference between the treatment and control group.

Table 4 shows correlations with the subjective well-being measures and the digital activities in block 1 (i.e., activities that are not affected by the treatment condition) for users with an Android device. We detail the correlations of the subjective well-being measures at the beginning of the first block (in survey 1) and at the end of the block (in survey 2) to study potential reverse causality, e.g., increased well-being leads so more/less social media activities or vice versa.

(Correlations in bold font are significant on a 5% level).

Satisfaction with life and mental well-being are positively correlated and are also significant predictors over time, i.e., subjective well-being in survey 1 is positively correlated with the same measure in survey 2. Regarding digital activities, we see, on average, negative correlations between subjective well-being and all digital activities, albeit not being significant. Similarly, we do not find significant correlations with social media use. (We will address causality in the section below.) To address potential non-linear effects we also report correlations with categories of social media usage. Specifically, we used dummy variables relating to low usage with an average of less than 2 minutes per day (36.1% of users), medium usage of 2 to 20 minutes (39.5%), and high usage of 20 minutes or more (24.4%). A non-linear relationship is likely as low usage generally shows the most negative subjective well-being scores, while medium usage and not high usage scores the highest well-being. However, we cannot rule out reverse causality regarding these findings as the only significant relationships are between satisfaction with life measured in survey 1 and the social media activities measured after the survey has taken place.

Activities related to communication, i.e., instant messaging and email, show significant positive correlations in the second surveys (for instant messaging regarding mental well-being and for email in terms of satisfaction with life). A consistent significant negative correlation can be observed for activities in the video category and satisfaction with life (both surveys) and mental well-being (survey 1). This suggests that less satisfied students and those with lower mental well-being at the beginning of the block increasingly watch videos in the subsequent block.

Academic performance

The students participating in our study scored an average (SD) of 7.105 (1.078) in block 1 and 7.122 (0.954) in block 2. Most grades can be classified as “standard”. The average (SD) sum of credit points per block is 13.571 (5.754) in block 1 and 12.353 (5.118) in block 2. Differences between the treatment and control group are not significant for grades (t-test, p = 0.113) but for the number of credit points such that the treatment group attempted to score significantly more credits (t-test, p = 0.035). This is visualized in Fig 4 as the difference in grades and credits in block 1 and 2. Note that the number of ECTS represents the courses that the student attempted to pass but they are also stored if the student failed the exam. A comparison of the number of successfully passed courses shows no significant differences between the groups (t-test, p = 0.383).

Table 5 shows the correlations of academic performance in block 1 with the subjective well-being measures and digital activities. Accordingly, grades in block 1 are positively related to grades in block 2. The grades are not significantly correlated with the number of credit points, possibly due to a trade-off regarding a good grade and completing more courses. Credits are also not positively related over time, which is reasonable as more credits in one term means that the students have to obtain fewer credits in subsequent terms. The average grade is positively and significantly correlated with satisfaction with life measures. This holds for SWLS measures at the beginning and end of the block.

Regarding correlations with digital activities we can observe significant positive effects on the average grade for writing and presentation activities, which are required to complete assignments (that are part of the grade for the majority of courses). We also see a positive effect of instant messaging on the grade, however, a negative effect on the number of credit points. Social media usage is not significantly correlated with the academic performance in block 1. Only when categorizing students based on their social media usage we see significant effects such that medium usage is negatively correlated with the number of credits.

To what extent these findings can be interpreted as causal evidence will be addressed in the following section by analyzing the complete randomized control trial across the two blocks using difference-in-differences analyses with regression models.

Regressions

Tables ​ Tables6 6 and ​ and7 7 show the results of the regression models that use subjective well-being ( Table 6 ) and academic performance measures ( Table 7 ) as the dependent variables. We use data from the two teaching blocks with “block 2” being a dummy variable indicating the block in which the treatment took place. Similarly, the “treatment group” refers to a dummy variable that identifies students that were exposed to the treatment. We use gender, age, number of years at the university, and whether the student is working next to the studies (dummy variable) as control variables.

Regarding subjective well-being measures SWLS and SWEMWBS, we do not see any significant effects due to using Facebook, Instagram, and Snapchat less. The (Treatment group * Block2 interaction) that indicates if the treatment group differed from the control group in block 2 remains insignificant, irrespectively of the well-being measure (SWLS or SWEMWBS) or sample used (full sample or Android subset). Only few predictors are significant (gender in the full sample, p = 0.013 or age in the Android subset, p = 0.026; both for the SWEMWBS) but these are not directly related to the experimental setting. Overall, the amount of variance explained remains rather low and ranges between 3.2% (SWLS measure, full sample) and 14.2% (SWEMWBS, Android sample).

There is also no evidence that the treatment group achieves higher grades in block 2 (Treatment group * Block2 interaction), in which social media usage was restricted, than the control group (p = 0.239). This also holds for other subsets of the sample such as students with an Android device. Overall, the amount of variance explained in the grades remains very low with 3.4%. Only the number of years that the student has spent at the university is a significant positive predictor, i.e., Master’s students achieve higher grades than Bachelor’s.

In terms of number of credit points, i.e., number of courses attended, we do see a significant effect of restricting social media. While students overall attended courses with fewer credits in block 2 (p = 0.004) this is not the case for students in the treatment group such that they targeted significantly more credits (p = 0.023). With this dependent variable, the number of years at the university has a significant negative effect (p = 0.002) and, overall, 11.7% of variance in the number of credit points can be explained. The subset of Android users replicates these results, albeit generally with lower levels of significance due to the smaller sample size.

However, as noted above, the number of credit points does not necessarily show that students successfully passed more courses as also failed courses are included. Using the number of courses passed as the dependent variable shows that the students in the treatment group in fact do not differ from the control group (a Poisson model replicates these results). Thus, it appears that the treatment group attempted to pass more courses or courses with more credits compared to the control group but did not necessarily succeed.

These null results of the effects of restricting social media usage on academic performance and subjective well-being raises the question of whether our study is underpowered to detect economically significant effects. At 80% power the minimum detectable difference in life satisfaction (on the SWLS scale) with our sample size is 2.5 on a scale of 5–35 (average life satisfaction score in our sample is 25 with a standard deviation of 5). The SWLS categories comprise 5 scale points, for example, a score of 25–29 is considered as a “high” score. Therefore, even if the treatment group’s life satisfaction score increased by 1.5 it is generally not sufficient to change the classification from one category to another. The minimum detectable difference in average grade is 0.5 on a scale of 1–10 (average grade in our sample is about 7 with a standard deviation of 1). Given that students receive grades which are whole numbers, this threshold is still below the value that would increase the treatment group’s average grade by a full point. At least, our results indicate that changes in grades and subjective well-being are not substantial so that they could be detected with our sample size.

To further address potential concerns about statistical power we applied a hierarchical Bayes ANOVA (BANOVA) model that includes between and within subject effects and accommodates unobserved heterogeneity by including a normal distribution of the parameters across individuals [ 51 ]. All models converge and generally replicate the results above (details are available from the authors upon request).

Post-treatment effects

The formal analysis of the post-treatment effects is based on a sample of 106 students who provided activity data throughout all three blocks and in all four surveys. While the treatment condition significantly reduced their social media usage in block 2 compared to the control group, this effect was not permanent. After we suspended the limit in block 3 the social media activities of users in the treatment group increased again, showing no significant differences to the control group any longer (t-test, p = 0.668). We further do not see any significant differences between the treatment and control group in block 3 in terms of grades (t-test, p = 0.152), number of credit points (t-test, p = 0.923), satisfaction with life (t-test, p = 0.499), or mental well-being (t-test, p = 0.966), i.e., there is no lagged effect of reduced social media usage.

In this paper, we analyzed the effects of restricting social media usage. We did not find significant causal effects of social media usage on well-being or academic performance, other than students attempting (but not succeeding) to pass more courses or courses with more credits. However, we found robust evidence of substitution effects that can potentially explain the null finding. Specifically, we showed that social media and instant messaging apps can be substitutes. The European Commission approved Facebook’s acquisition of WhatsApp in 2014 based on Facebook’s claim that it operates in a different market and does not compete directly with WhatsApp [ 52 ]. Our results indicate that they are in fact direct competitors. After acquiring WhatsApp, Facebook started automatically matching its users’ profiles with their WhatsApp accounts and has started integrating WhatsApp, Instagram and Facebook user accounts [ 53 ]. The European Commission fined Facebook €110 million in 2017 for this practice because Facebook had provided misleading information about the feasibility of automatically matching profiles during its acquisition of WhatsApp. However while announcing this fine, the Commission still maintained its belief that Facebook and WhatsApp do not directly compete with each other [ 54 ]. Antitrust authorities should consider the market power of this combined entity if the world’s biggest social media platforms are integrated with the world’s biggest instant messaging platform. This finding also raises the question how to properly define social networks, for future academic studies or antitrust cases.

While we found null results estimating the causal impact of social media usage on well-being and academic performance, and not all null results matter, we believe that null results are interesting and important in this context. The media has hyped correlational studies showing a negative association between social media usage and well-being and it is important to balance this narrative through causal evidence. A limitation of our study is the lack of a larger sample size to detect smaller effects. While these small effects might not be economically significant, more research is needed using massive samples. Future research can also look at differences between Android and iOS users in more detail with larger samples since we lack power to analyze these differences in our current study. However, it is challenging to recruit a large number of subjects from a representative sample for a long-term study. Direct collaborations with social media platforms or internet service providers (which control internet traffic) could be a way of obtaining data from larger samples. These would also facilitate more targeted interventions such as restricting only content consumption or social interactions on social media [ 18 ].

It is interesting to notice that while social media generates large amount of consumer surplus [ 3 ], it doesn’t seem to affect the subjective well-being of users. Future research can explore this wedge between consumer surplus and subjective well-being and see whether they are correlated for some products and uncorrelated for others. Future research should also explore the addictiveness of social media in more detail [ 55 ]. Our findings in block 3 show that the students in the treatment condition go back to their old habits and do not adopt a lower social media usage that they experienced in block 2. On the other hand, showing students how much time they are spending on social networks via the software seems to have an overall negative trend on its usage (comparing usage in block 1 and block 3). Curing social media addiction (if it is indeed addiction) might therefore be a longer process. Future research can look at mechanisms for the emergence of social media addiction, for e.g. through targeted advertising or news feed algorithms and features of social media apps (e.g. video sharing) which are correlated with addiction.

Moreover, due to our student sample implications for the general population are limited. It could be that students use social media mostly for communication purposes and therefore show significant substitution effects with instant messaging. We might see different effects for users who visit social media for content consumption, e.g., watching videos, instead of social interaction [ 18 ]. In this regard, newer social media services such as TikTok might have a different effect. However, these and other comparable social media services were not relevant at the time we conducted the study (as we measured in block 1). More research is needed looking at emerging social media platforms such as TikTok. Moreover, one could define social media more broadly to include instant messaging services and implement a stricter restriction involving all types of communication and social networking apps. However, conducting such a study for any length of time is a challenging endeavor.

Additionally, while we study the impact of social media on students and academic performance, future research can look at workplace settings and study the impact of social media and its substitutes on worker productivity and well-being. We believe that rigorous causal evidence through randomized controlled trials and objectively measured time spent is the way forward in addressing questions regarding the impact of technology on well-being.

The widespread adoption of most major technologies in the past such as radio, television, video games and computers was followed with unfounded fears about their impact on well-being. This story repeats again with social media. We find that social media usage does not cause lower well-being or poor academic performance. Rather, we demonstrate that students find other means of social networking using instant messaging when exogenously restricting their social media usage. To conclude: You can take social networking away from the students, but you cannot take students away from their social network.

Supporting information

a Tracked digital activities over time (all users). b. Tracked digital activities over time (all users).

Funding Statement

A.C. received funding from the MIT Initiative on the Digital Economy for this research ( https://ide.mit.edu/ ). There is no associated grant number. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. F.E. received funding from the Behavioral Research Lab at the Faculty of Economics and Business at the University of Groningen for this research ( https://www.rug.nl/feb/?lang=en ). There is no associated grant number. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Data Availability

• PLoS One. 2022; 17(8): e0272416.

Decision Letter 0

20 Aug 2021

PONE-D-21-20386

Effects of restricting social media usage

Dear Dr. Collis,

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Additional Editor Comments

We have received two reviews that I think will be helpful to you if you wish to pursue publication. In particular, both reviewers think your study is under-powered considering the typical effect size that is observed in the literature. This may be a serious obstacle to moving ahead with the study as it stands. However, R2 suggests that perhaps a multi-level analysis may increase your power and thus overcome this problem,. R1 also suggests that it may be valuable to examine effects in terms of mediators that have produced different effects and thus hindered your ability to observe an effect (i.e., the type of content that is accessed). If this objection can be overcome, then it may be possible to salvage your study. In addition, both reviewers think that your study is limited in so far as it only required reductions in use of three platforms. Can you respond to this concern by looking at what the students actually did in each condition? Obviously, if the experimental group merely displaced use to other platforms, then the findings are much more limited in terms of overall social media use. In addition, from my own perspective, I would look to see if any effects are more likely among those who were already experiencing mental health problems because withdrawal from in-person interaction is common among those with depression and related conditions. it may be that they are the ones who are likely to show stronger effects of not using the one socializing outlet they find attractive. Finally, R1 asks for more disclosure of findings that would be helpful to others who would want to include your findings in a synthesis of results, and both reviewers feel that important prior papers are being ignored in your literature review.

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Reviewer #2: Partly

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Reviewer #1: Review for PONE-D-21-20386 : Effects of restricting social media usage

This paper describes a random assignment experiment comparing the subjective well-being (satisfaction with life and mood) and grades of university students who were asked to reduce their social media use (Facebook, Instagram, Snapchat) to less than 10 minutes per day with a control group who were not asked to make social media adjustments. Results show no significant differences between the reduction group and control group on these outcomes. (Because I am not familiar with work that on social media use and educational outcomes, I will ignore it in this review.)

As the authors note, prior research on the impact of social media use on subjective well-being has primarily involved cross-sectional surveys, correlating self-reports of social media use with self-reports of well-being. Moreover, again as the authors note, results from the correlational research have been mixed, although the most recent meta-analysis reviewing 94 studies (Liu et al., 2019) suggests that on average social media use is negatively associated with subjective well-being. However, the association is substantially dependent on type of use (e.g., r=.14 for social interaction versus -.14 for content consumption). Thus, a random assignment experiment is an excellent addition to this body of work.

The authors introduce their research with the claim for novelty. “To the best of our knowledge, this study is the first that tracks all of these components of well-being and over a long period of time.” This claim is overstated. I am familiar with five experiments, in which participants were randomly assigned to reduce their social media use for one to four weeks (Allcott et al., 2020; Brailovskaia et al., 2020; Hanley et al., 2019; Mosquera et al., 2019; Vanman et al., 2018). The current research improves on prior experiments by upping the ante to nine weeks. Although some of the prior experiments used small student samples like the current study, others used much larger and more diverse samples. All of them show that social media restrictions led to better subjective well-being. Of these, the current authors cite only the Allcott paper, but criticize it because it only reduced social media use for a four-week period.

I have some substantive methodological concerns about the paper involving the conceptualization of the intervention, statistical methods, and the power of the intervention. Given the prior literature, which suggests that the impact of social media use depends upon the functions for which it is used and not just the total amount of use, I am disappointed that the current research did not target more precisely the type of social media use to restrict. For example, a 2 X 2 factorial experiment (reduce social interaction vs control (reduce content consumption vs control) would have been more informative than the current experiment, which restricted overall use, and could have been done even with a relatively small sample size of 130 completed participants.

I also disappointed on the way that the research defined social media use for participants. The research considered Facebook messenger (a popular application on Facebook), Instagram and Snapchat, all of which are used for one-on-one or small group communication, as social media applications while WhatsApp, another messaging application, is not classified as social media.

According to the methods description on page 13 and Tables 4 and 5 in the manuscript, the authors have some data on what activities participants used across the platforms. I think the authors could more strongly advance the prior literature about the differential importance of types of use and still take advantage of their random assignment experiment by using instrumental variable regression or mediation models to analyze the data. For example, the authors could use the experiment to dig more deeply into the conclusion form Liu et al’s meta-analysis that social interaction and content consumption have different effects on well-being. An instrumental variable regression analysis could examine the extent to which the experimental manipulation changed participants’ type of use (e.g., instant messaging and email as proxies for use for social interaction vs general news, general shopping, and video as proxies for content consumption) and how the changes in in use for social interaction and content consumption use caused by the manipulation predict well-being and grades.

I have two concerns about the power of the experiment. First, the manipulation check indicates that the experimental manipulation did not have a major impact on the amount of time participants spent on social media, with participants in the experimental condition reducing time spent on the three predefined social media applications by 13 minutes versus 3.7 minutes for participants in the control condition. Given the substitution behavior the authors document, in which participants in experimental condition started using WhatsApp more, the experimental manipulation did not reduce overall social media use. Instead, it caused shifts in the types of social media applications used, if one considers Facebook, Instagram, Snapchat, and WhatsApp all as social media platforms. This shift in behavior is another reason I recommend that the authors consider an instrumental variable regression model to test their hypotheses.

Second, both the correlational and experimental prior literature suggests that the relationship between social media use and well-being is likely to be small, with the average absolute correlation being -.10 in the Liu meta-analysis of correlational studies and a .09 standard deviation (STD) effect size in the Allcott et al experiment. Given the likely small effect size, an experiment with 130 participants is probably underpowered to detect this effect. The authors’ discussion of their power analysis indicates that their study could detect a .6 STD effect on the SWLS Life Satisfaction scale, substantially larger than the effect size one might have anticipated from the prior literature. While the authors acknowledge this limitation, they still interpret their failure to find a significant effect of their restriction intervention much more seriously than it deserves. As their paper states, “Contrary to findings from previous correlational studies, we do not find any impact of social media usage on wellbeing and academic success. … While we found null results estimating the causal impact of social media usage on well-being and academic performance, and not all null results matter, we believe that null results are interesting and important in this context. The media has hyped correlational studies showing a negative association between social media usage and well-being and it is important to balance this narrative through causal evidence.” Indeed, even though the experiment found no significant effects of their intervention on either satisfaction with life or mental well-being, the treatment X Block2 effects are both negative and consistent with the prior literature. As part of this review, I had intended to conduct a quick and dirty meta-analysis of the experimental literature to date (i.e., the current paper plus the five additional experiments listed below) but discovered that the authors of the current paper don’t provide enough information to include their research in the meta-analysis. Page 17, where the authors present their well-being results, should include a table of means and standard deviations for the SWLS and SWEMWBS scores for Block 2 broken down by experimental condition. Table A-3, which only reports the regression coefficients and p-values does not provide sufficient information to reconstruct the needed information.

In summary, this is an interesting study with a laudable goal. Experimental evidence is hugely important, and the current study supplements other recent experiments restricting social media usage by throttling back usage over a 9-week period, compared to a maximum of four weeks in other work. However, the study has some important flaws that make me believe the paper shouldn’t be published in its current form. The authors made arbitrary decisions about what online behavior to consider use of social media (i.e., only Facebook, Snapchat, and Instagram, but not WhatsApp), failed to differentiate types of use (e.g., social interaction versus content consumption) even though the prior literature suggest that type of use is an important moderator, and was under powered. A more minor problem is that the authors don’t provide enough information to allow their research to be used in a meta-analysis. I think the paper still has promise but needs to do more sophisticated data analysis (e.g., Instrumental variable approach mentioned previous or a mediation analysis) to link the changes induced by the experimental manipulation on well-being.

Allcott, H., Braghieri, L., Eichmeyer, S., & Gentzkow, M. (2020). The welfare effects of social media. American Economic Review, 110(3), 629-676.

Brailovskaia, J., Ströse, F., Schillack, H., & Margraf, J. (2020). Less Facebook use–More well-being and a healthier lifestyle? An experimental intervention study. Computers in Human Behavior, 108, 106332.

Hanley, S. M., Watt, S. E., & Coventry, W. (2019). Taking a break: The effect of taking a vacation from Facebook and Instagram on subjective well-being. PloS one, 14(6), e0217743.

Liu, D., Baumeister, R. F., Yang, C.-c., & Hu, B. (2019). Digital communication media use and psychological well-being: A meta-analysis. Journal of Computer-Mediated Communication, 24(5), 259-273.

Mosquera, R., Odunowo, M., McNamara, T., Guo, X., & Petrie, R. (2019). The economic effects of Facebook. Experimental Economics, 1-28.

Vanman, E. J., Baker, R., & Tobin, S. J. (2018). The burden of online friends: The effects of giving up Facebook on stress and well-being. The Journal of social psychology, 158(4), 496-508.

Reviewer #2: The paper submitted to PLOS One investigates whether voluntary restriction of social media usage to 10 minutes per day has any effects on a) subjective well-being and b) academic performance. To this end, the authors conducted an randomised controlled trial in which half of 121 students were asked to restrict their social media usage (i.e., the use of social network sites Facebook, Instagram and Snapchat) to a maximum of 10 minutes per day over a period of 1.5 months. Overall, the authors did not find any effects of social media usage restriction on subjective well-being or academic performance.

First, I would like to highlight that I deem the topic – social media uses effects on well-being/academic performance – and the proposed experimental approach to study it – experimentally restricting social media use – highly important and innovative. The debate around effects of social media use or screen time on well-being is on-going, both in public and scientific circles. And the authors are right to point out that much of it is based on questionable, correlational evidence. From this point of view, I welcome the submission. Second, the experiment is well designed and conducted, the analyses seem for the most part sound, and the paper overall well-written. The author neither make to strong claims, nor fail to articulate the contribution. As such, I do believe that the paper has the potential to add to the literature.

However, I do see several problems and issues, most minor but some also major, that require revision.

- First and foremost, I am concerned that the authors did not conduct comprehensive a priori power analyses. The sample sizes after each block are comparatively small and hardly sufficient to test effects of reduced social media use on general and stable outcomes such as well-being or academic performance. In other words, the study seems heavily underpowered to study the effects of interest. I believe that this is a major issues that unfortunately might not be resolvable. I thus recommend the authors to consider conducting another study with a more appropriate sample size. When doing so, I would also recommend to preregister all assumptions and analysis plans.

- Second, although the authors provide a summary of related literature, it – from my point of view – does not reflect the current state of the debate sufficiently. A lot of major contributions in the past years are missing and some similar experiments are not mentioned (among others, I was particularly missing reference to the work of Jeffrey Hall, 2021).

- Another issue relates to a lack of transparency in reporting. Some important aspects of the data analysis are not reported (e.g., missing data analysis and treatment, exact tests and covariates...). Information about the used tracking app (including how well the capturing of data worked) is missing completely. I appreciate that the authors explain why the data cannot be shared, but I strongly recommend to make everything else (material, instructions, information about the app, analysis code, etc.) available to the reader.

In the following, I will highlight more specific issues by going through the manuscript chronologically.

pp. 3-9.: As mentioned above, I am missing a comprehensive discussion of the current state of the literature. With regard the relationship between social media use and well-being, consider referring to the meta-analyses Huang (2010, 2017), Liu, Baumeister, Yang & Hu (2019), as well as the reviews by Appel, Marker & Gnambs (2020), Meier & Reinecke (2021), Dienlin & Johannes (2020), Masur (2021) and Orben (2020). Consider also looking at more recent studies on the relationship, e.g., Schemer et al. (2020), Kim & Chen (2020), Johannes et al. (2020). With regard to experimental studies on effect of social media abstinence, I was further missing reference to the work by Jeffrey Hall (2021) and the earlier works by Hinsch & Sheldon (2013) and Tromholt (2016). I strongly recommend that the authors revise this sections considerably.

p. 9: Consider explicitly formulating your hypotheses. In its current form, I find it difficult to understand what you are exactly testing.

p. 10: What was the exact reason to assess participants at 3 different time points? And why did you not incorporate this in your analysis? Wouldn't it be possible to run some sort of time-series or multi-level analysis on the entire data set? This way, you could potentially also increase your power (by combining within- and between-perspectives).

p. 11: The paper is framed as "reducing social media use". The term social media is quite broad. Often, it even includes instant messaging and other types of platforms. In light of this, why only restricting Facebook, Instagram and Snapchat use? What about TikTok, Twitter, Pinterest, Youtube? I suggest to include clear definition of social media and justify your choice of platforms. This may have important implications for how to frame the entire study and how to intrepret the results as well: I would be careful to generalize on all social media use given that the treatment was restricted to three platforms (albeit the most used ones).

p. 12: Although perhaps not very common, I would appreciate you reporting comprehensive factor analyses (CFAs, etc) for both well-being scales. Despite them being used a lot, they often show considerable misfit with the data.

p. 13: What type of software/application was used? Please provide more information and validity checks. Did the tracking work perfectly? From my experience, one often has some problems in the data with such software.

p. 14: What was your justification for the sample size? As mentioned earlier, I am concerned that your study is heavily underpowered. Further, how did you deal with missing data? Did all 122 participants answers all items in all waves?

p. 15: In reporting the results, it is often unclear what tests of analytical approaches were chosen. Please indicate more clearly which results relate to which analysis. Are the p-values related to simple t-tests? Or more elaborate ANOVAS including covariates? This should be made more clearer. Please report unstandarized and standardized coefficients (effect sizes) as well. Significance alone is not meaningful.

Appel, M., Marker, C., & Gnambs, T. (2020). Are social media ruining our lives? A review of meta- analytic evidence<https: 10.1177="" 1089268019880891="" doi.org ="">. Review of General Psychology, 24(1), 60–74.

Dienlin, T., & Johannes, N. (2020). The impact of digital technology use on adolescent well- being<https: 10.31887="" dcns.2020.22.2="" doi.org ="" tdienlin="">. Dialogues in Clinical Neuroscience, 22(2), 135–142.

Hall, J. A., Xing, C., Ross, E. M. & Johnson, R. M. (2021) Experimentally manipulating social media abstinence: results of a four-week diary study.Media Psychology, 24:2, 259-275, DOI: 10.1080/15213269.2019.1688171

Hinsch, C., & Sheldon, K. M. (2013). The impact of frequent social internet consumption: Increased procrastination and lower life satisfaction. Journal of Consumer Behaviour, 12, 496–505. doi:10.1002/cb.1453 

Huang, C. (2010). Internet use and psychological well-being: A meta-analysis. Cyberpsychology, Behavior, and Social Networking, 13(3), 241–249.

Huang, C. (2017). Time spent on social network sites and psychological well-being: A meta- analysis<https: 10.1089="" cyber.2016.0758="" doi.org ="">. Cyberpsychology, Behavior, and Social Networking, 20(6), 346–354.

Johannes, N., Meier, A., Reinecke, L., Ehlert, S., Setiawan, D. N., Walasek, N., Dienlin, T., Buijzen, M., & Veling, H. (2020). The relationship between online vigilance and affective well-being in everyday life: Combining smartphone logging with experience sampling<https: 10.1080="" 15213269.2020.1768122="" doi.org ="">. Media Psychology.

Kim, C., & Shen, C. (2020). Connecting activities on social network sites and life satisfaction: A comparison of older and younger users<https: 10.1016="" doi.org ="" j.chb.2019.106222="">. Computers in Human Behavior, 105, 106222.

Liu, D., Baumeister, R. F., Yang, C., & Hu, B. (2019). Digital communication media use and psychological well-being: A meta-analysis<https: 10.1093="" doi.org ="" jcmc="" zmz013="">. Journal of Computer-Mediated Communication, 24(5), 259–273.

Masur, P. K. (2021). Digital Communication Effects on Loneliness and Life Satisfaction. In J. Nussbaum (Ed.), Oxford Research Encyclopedia of Communication. Oxford University Press. https://doi.org/10.1093/acrefore/9780190228613.013.1129

Meier, A., & Reinecke, L. (2020, October 21). Computer-mediated communication, social media, and mental health: A conceptual and empirical meta-review<https: 0093650220958224="" 10.1177="" doi.org ="">. Communication Research.

Orben, A. (2020b). Teenagers, screens and social media: A narrative review of reviews and key studies<https: 10.1007="" doi.org ="" s00127-019-01825-4="">. Social Psychiatry and Psychiatric Epidemiology, 55(4), 407–414.

Schemer, C., Masur, P. K., Geiss, S., Müller, P., & Schäfer, S. (2020). The impact of internet and social media use on well-being: A longitudinal analysis of adolescents across nine years<https: 10.1093="" doi.org ="" jcmc="" zmaa014="">. Journal of Computer-Mediated Communication, 26(1), 1–21.

Tromholt, M. (2016). The Facebook experiment: Quitting Facebook leads to higher levels of well-being. Cyberpsychology, Behavior, and Social Networking, 19(11), 661–666. doi:10.1089/cyber.2016.0259</https:></https:></https:></https:></https:></https:></https:></https:></https:>

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Submitted filename: PONE-D-21-20386-review.pdf

Author response to Decision Letter 0

14 Mar 2022

We uploaded a document responding point by point to each editor and reviewer comment.

Decision Letter 1

13 Apr 2022

PONE-D-21-20386R1Effects of restricting social media usage on wellbeing and performance: A randomized control trial among studentsPLOS ONE

Thank you for making the extensive changes to your paper in response to the reviewers’ and my suggestions. The paper is much stronger but I have a few more suggestions before moving on to making a decision on publication.

First, a very real concern is the statistical power of your study, and I don’t think your description of that problem on page 27 is very clear or helpful. First of all, we don’t usually consider power from the point of view of the maximum difference we cannot detect. It would be easier to understand this if you talked about the minimum difference you can detect. Second, I am puzzled as to why you are talking about increases in life satisfaction, when the concern is for decreases. Also, should you not be citing the differences you observed and discussing whether you could have found those to be significant with more power? That is what seems to be the issue. Given that you found a slight increase among the Android group, it might make sense to look at both groups separately in considering the power question. I would also recommend discussing power in the method section with how large a difference you will be able to detect given the sample size you have. Then in the Results, you could comment on whether your differences are large enough to have rejected the null hypothesis with more power.

I am also puzzled as to why you show the regressions for grades but not for wellbeing and life satisfaction in the Results. It seems to me that the mental health concerns are the ones that motivated this to begin with, so those findings should receive priority.

Finally, I think your findings could use more discussion than you devote to them. It seems that your intervention to reduce some forms of social media use served to increase other forms of interaction within one’s social network. So, what your findings seem to say is that social media may primarily serve as a way to connect with others and that arbitrarily defining those media as you have merely caused your students to migrate to other platforms that serve the same purpose. In other words, what your study shows is that with all of the ways that people can interact online, it is difficult to isolate some as more responsible for adverse effects. You would have to restrict the ability to use online platforms to communicate with others as a way to isolate that effect and I doubt that you could recruit a sample to do that for any length of time.

One other possible way that some social media may be harmful is by pushing advertising to users that they may not want exposure to and doing this to maintain continued engagement.  This might be a mechanism for internet addiction that could be studied. In any case, I think a deeper discussion of these issues would be helpful for readers. Your point about the policy implications also follows from this, since many online media serve the same purpose and just differ in how they afford that opportunity.

I also think you have more findings to discuss, such as correlations between mental health and various uses of social and other media, such as in Table 4. Perhaps it is the video features of online media that are the source of adverse effects on mental health? And perhaps the sites push certain videos in order to maintain engagement?

In the abstract, I would describe the finding by saying that “we do not find any significant impact of social media usage as it was defined in this study on well-being…” As part of your limitations section, I would also take note of the fact that other forms of social media were not excluded in your study and this will be an obvious point of criticism.

I also think it would be useful to consider a paper that just came in Clinical Psych Science by Sewall et al. They also find that various uses of digital tech are not very strongly related to mental health in young adults

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Author response to Decision Letter 1

28 Jun 2022

Please see the attached document.

Submitted filename: Response to Reviewers.pdf

Decision Letter 2

20 Jul 2022

PONE-D-21-20386R2

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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I found two typos and there may be more. i am attaching the pdf with those places highlighted.

Submitted filename: PONE-D-21-20386_R2[11].pdf

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University of Utah announces major funding for new addiction treatment research

Media contact:.

Patricia Brandt Manager, Public Relations and Communications, Huntsman Mental Health Institute University of Utah Health Email: Patricia.Brandt @hsc.utah.edu

Salt Lake City (April 10, 2024) - Worldwide, someone dies from drug or alcohol addiction every four minutes. Now, researchers at Huntsman Mental Health Institute at the University of Utah have been selected by Wellcome Leap to research a new treatment for substance use disorder as part of a $50 million commitment to develop innovative treatments.

Brian J. Mickey, MD, PhD, professor of psychiatry at Huntsman Mental Health Institute (pictured top left), will lead the team of investigators with expertise in psychiatry, biomedical engineering, neuroscience, radiology, and social work to research a new, noninvasive treatment for addiction. Co-principal investigators include Jan Kubanek, PhD , (pictured top center), and Taylor Webb, PhD (pictured top right); co-investigators include (from left to right) Eric Garland, PhD, LCSW ; Rana Jawish, MD ; Vincent Koppelmans, PhD ; and Tom Riis, PhD.

The research will be funded by the Untangling Addiction program, which is a $50 million program founded by Wellcome Leap , to develop scalable measures to assess addiction susceptibility, quantify the risks stemming from addiction, and develop innovative treatments.  

“Substance use disorder is a significant global health problem, and yet the treatment options are limited,” Mickey said.  “We’re developing a non-invasive intervention for preventing and treating addiction, chronic pain, and depression. This funding will help us validate and generate the data to support the next critical step: an efficacy trial to determine the effectiveness of the intervention.”

Mickey’s team will use a novel ultrasound-based device to modulate deep brain regions and behaviors associated with opioid addiction. The goal will be to ultimately develop this approach into an individually targeted therapeutic intervention for a range of addictions. “Addictions are brain illnesses that have enormous negative impact on individuals, families, and society,” Mickey said. “A major reason that addictions have been difficult to prevent—and treat—is that they are driven by dysfunction of deep brain regions that are challenging to access. Many psychiatric problems such as depression, anxiety, and addiction are caused by malfunction of brain circuits. This project is an example of our mission to understand how these neural circuits are dysregulated and to develop novel, circuit-targeted interventions that return the brain to a healthy state.”

"We are proud to bring Wellcome Leap's innovative problem-solving and funding approach to our research enterprise at the University of Utah," said Taylor Randall, President , University of Utah. "To have our mental health researchers contributing to pioneering work on addiction treatment reaffirms our commitment to improving lives through discovery."

“What makes research like this so impactful is that it brings together a variety of disciplines to help solve complex problems in mental health,” said Mark Hyman Rapaport, MD , CEO of Huntsman Mental Health Institute. “This is particularly timely news given the groundbreaking of a new translational research building on campus focused on mental health and the brain. Our nation is in a mental health crisis, but there is hope if we can think differently and work together to change this trajectory.”

About Huntsman Mental Health Institute

Huntsman Mental Health Institute at University of Utah Health brings together 75 years of patient care, research, and education into one of the nation's leading academic medical centers focused on mental health. Nestled in the campus of University of Utah, Huntsman Mental Health Institute serves the community with 1,600 faculty and staff in 20 locations providing inpatient and outpatient services for youth, teens, and adults as well as a comprehensive crisis care model which includes the nationally recognized SafeUT app and the 988 Crisis hotline for Utah. Our mission is to advance mental health knowledge, hope, and healing for all. Learn more at:  HMHI.utah.edu  and join the conversation on  Instagram ,  Facebook ,  TikTok ,  X  and  LinkedIn .

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6 facts about americans and tiktok.

Increasing shares of U.S. adults are turning to the short-form video sharing platform TikTok in general and for news .

Pew Research Center conducted this analysis to better understand Americans’ use and perceptions of TikTok. The data for this analysis comes from several Center surveys conducted in 2023.

More information about the surveys and their methodologies, including the sample sizes and field dates, can be found at the links in the text.

Pew Research Center is a subsidiary of The Pew Charitable Trusts, its primary funder. This is the latest analysis in Pew Research Center’s ongoing investigation of the state of news, information and journalism in the digital age, a research program funded by The Pew Charitable Trusts, with generous support from the John S. and James L. Knight Foundation.

This analysis draws from several Pew Research Center reports on Americans’ use of and attitudes about social media, based on surveys conducted in 2023. For more information, read:

Americans’ Social Media Use

How u.s. adults use tiktok.

• Social Media and News Fact Sheet
• Teens, Social Media and Technology 2023

At the same time, some Americans have concerns about the Chinese-owned platform’s approach to data privacy and its potential impact on national security. Lawmakers in the U.S. House of Representatives recently passed a bill that, if passed in the Senate and signed into law, would restrict TikTok’s ability to operate in the United States.

Here are six key facts about Americans and TikTok, drawn from Pew Research Center surveys.

A third of U.S. adults – including a majority of adults under 30 – use TikTok. Around six-in-ten U.S. adults under 30 (62%) say they use TikTok, compared with 39% of those ages 30 to 49, 24% of those 50 to 64, and 10% of those 65 and older.

In a 2023 Center survey , TikTok stood out from other platforms we asked about for the rapid growth of its user base. Just two years earlier, 21% of U.S. adults used the platform.

A majority of U.S. teens use TikTok. About six-in-ten teens ages 13 to 17 (63%) say they use the platform. More than half of teens (58%) use it daily, including 17% who say they’re on it “almost constantly.”

A higher share of teen girls than teen boys say they use TikTok almost constantly (22% vs. 12%). Hispanic teens also stand out: Around a third (32%) say they’re on TikTok almost constantly, compared with 20% of Black teens and 10% of White teens.

In fall 2023, support for a U.S. TikTok ban had declined. Around four-in-ten Americans (38%) said that they would support the U.S. government banning TikTok, down from 50% in March 2023. A slightly smaller share (27%) said they would oppose a ban, while 35% were not sure. This question was asked before the House of Representatives passed the bill that could ban the app.

Republicans and Republican-leaning independents were far more likely than Democrats and Democratic leaners to support a TikTok ban (50% vs. 29%), but support had declined across both parties since earlier in the year.

Adults under 30 were less likely to support a ban than their older counterparts. About three-in-ten adults under 30 (29%) supported a ban, compared with 36% of those ages 30 to 49, 39% of those ages 50 to 64, and 49% of those ages 65 and older.

In a separate fall 2023 survey, only 18% of U.S. teens said they supported a ban. 

A relatively small share of users produce most of TikTok’s content. About half of U.S. adult TikTok users (52%) have ever posted a video on the platform. In fact, of all the TikTok content posted by American adults, 98% of publicly accessible videos come from the most active 25% of users .

Those who have posted TikTok content are more active on the site overall. These users follow more accounts, have more followers and are more likely to have filled out an account bio.

Although younger U.S. adults are more likely to use TikTok, their posting behaviors don’t look much different from those of older age groups.

About four-in-ten U.S. TikTok users (43%) say they regularly get news there. While news consumption on other social media sites has declined or remained stagnant in recent years, the share of U.S. TikTok users who get news on the site has doubled since 2020, when 22% got news there.

Related: Social Media and News Fact Sheet

TikTok news consumers are especially likely to be:

• Young. The vast majority of U.S. adults who regularly get news on TikTok are under 50: 44% are ages 18 to 29 and 38% are 30 to 49. Just 4% of TikTok news consumers are ages 65 and older.
• Women. A majority of regular TikTok news consumers in the U.S. are women (58%), while 39% are men. These gender differences are similar to those among news consumers on Instagram and Facebook.
• Democrats. Six-in-ten regular news consumers on TikTok are Democrats or Democratic-leaning independents, while a third are Republicans or GOP leaners.
• Hispanic or Black. Three-in-ten regular TikTok news users in the U.S. are Hispanic, while 19% are Black. Both shares are higher than these groups’ share of the adult population. Around four-in-ten (39%) TikTok news consumers are White, although this group makes up 59% of U.S. adults overall .

A majority of Americans (59%) see TikTok as a major or minor threat to U.S. national security, including 29% who see the app as a major threat. Our May 2023 survey also found that opinions vary across several groups:

• About four-in-ten Republicans (41%) see TikTok as a major threat to national security, compared with 19% of Democrats.
• Older adults are more likely to see TikTok as a major threat: 46% of Americans ages 65 and older say this, compared with 13% of those ages 18 to 29.
• U.S. adults who do not use TikTok are far more likely than TikTok users to believe TikTok is a major threat (36% vs. 9%).

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About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .