According to a March 2024 survey conducted in the United States, 32 percent of adults reported feeling that social media had neither a positive nor negative effect on their own mental health. Only seven percent of social media users said that online platforms had a very positive effect on their mental health, whilst the 12 percent of users said it had a very negative effect on them. Furthermore, 22 percent of respondents said social media had a somewhat negative effect on their mental health.

Data, collected in 2023 assess relationships between social media, mental health, and sleep health.

According to a survey conducted in the United States in March 2024, 35 percent of adults reported that they had taken an extended break from social media because it was harming their mental health. Overall, 51 percent of respondents had never taken an extended break from social networks for mental health reasons.

This study explores the intricate relationship between social media usage and the mental health of young individuals, leveraging the insights of 492 UK school headteachers. It adopts a novel multidisciplinary approach, integrating perspectives from psychology, media studies, sociology, anthropology, linguistics, social work, philosophy, and education. The application of thematic analysis, powered by ChatGPT-4, identifies a mostly negative perspective on the impact of social media on young people, with a focus on key themes across a number of disciplines including mental health and health themes, identity formation, social interaction and comparison, bullying, digital literacy, and governance policies. These findings culminated in the development of the five-factor Comprehensive Digital Influence Model, suggesting five key themes (Self-Identity and Perception Formation, Social Interaction Skills and Peer Communication, Mental and Emotional Well-Being, Digital Literacy, Critical Thinking, and Information Perception, and Governance, Policy, and Cultural Influence in Digital Spaces) to focus the impacts of social media on young peoples’ mental health across primary and secondary educational stages. This study not only advances academic discourse across multiple disciplines but also provides practical insights for educators, policymakers, and mental health professionals, seeking to navigate the challenges and opportunities presented by social media in the digital era

Suicide is not only an individual phenomenon, but it is also influenced by social and environmental factors. With the high suicide rate and the abundance of social media data in South Korea, we have studied the potential of this new medium for predicting completed suicide at the population level. We tested two social media variables (suicide-related and dysphoria-related weblog entries) along with classical social, economic and meteorological variables as predictors of suicide over 3 years (2008 through 2010). Both social media variables were powerfully associated with suicide frequency. The suicide variable displayed high variability and was reactive to celebrity suicide events, while the dysphoria variable showed longer secular trends, with lower variability. We interpret these as reflections of social affect and social mood, respectively. In the final multivariate model, the two social media variables, especially the dysphoria variable, displaced two classical economic predictors – consumer price index and unemployment rate. The prediction model developed with the 2-year training data set (2008 through 2009) was validated in the data for 2010 and was robust in a sensitivity analysis controlling for celebrity suicide effects. These results indicate that social media data may be of value in national suicide forecasting and prevention.

IntroductionWhile increased time spent on social media can be negatively related to one’s overall mental health, social media research often fails to account for what behaviors users are actually engaging in while they are online. The present research helps to address this gap by measuring participants’ active and passive social media behavioral styles and investigates whether and how these two social media behavioral styles are related to depression, anxiety, and stress, and the mediating role of emotion recognition ability in this relationship.MethodsA pre-study (N = 128) tested whether various social media behaviors reliably grouped into active and passive behavioral styles, and a main study (N = 139) tested the relationships between social media use style, emotion recognition, and mental health.ResultsWhile we did not find evidence of a mediating relationship between these variables, results supported that more active social media use was related to more severe anxiety and stress as well as poorer emotion recognition skill, while passive social media use was unrelated to these outcomes.DiscussionThese findings highlight that, beyond objective time spent on social media, future research must consider how users are spending their time online.

People with mental health conditions have been identified as particularly vulnerable to poor mental health during the coronavirus disease 2019 (COVID-19) pandemic. However, why this population have faced these adverse effects, how they have experienced them and how they have coped remains under-explored. To explore how the COVID-19 pandemic affected the mental health of people with existing mental health conditions, and to identify coping strategies for positive mental health. Semi-structured qualitative interviews with 22 people with mental health conditions. Participants were purposively recruited via social media, study newsletters and third sector mental health organisations. Data were analysed using reflexive thematic analysis. Participants were aged 23–70 (mean age 43), predominantly female (59.1%) and of white ethnicity (68.2%). Fifty percent were unable to work due to illness and the most frequently reported mental health condition was depression. Five pandemic-related factors contributed to deteriorating mental health: (i) feeling safe but isolated at home; (ii) disruption to mental health services; (iii) cancelled plans and changed routines; (iv) uncertainty and lack of control; (v) rolling media coverage. Five coping strategies were identified for maintaining mental health: (i) previous experience of adversity; (ii) social comparison and accountability; (iii) engaging in hobbies and activities; (iv) staying connected with others; (v) perceived social support. Challenges were identified as a direct result of the pandemic and people with severe mental illnesses were particularly negatively affected. However, some found this period a time of respite, drew upon reserves of resilience and adapted their coping strategies to maintain positive well-being.

According to a survey conducted in England in 2021, 20.6 percent of young people with a likelihood of probable mental disorder agreed to the statement that the number of likes, comments or shares they get on social media has an impact on their mood. While 63.8 percent of respondents with probable mental disorder agreed that they spent more time on social media then they meant to.

The diffusion of social type coincided with a worsening of mental health conditions on adolescents and young adults in the United Says, donating rise to speculation that social media might shall detrimental to mental health. In this paper, we provide quasi-experimental estimates of to impact of socially media on mental health by leveraging a unique natural experiment: the staggered introduction of Facebook across U.S. colleges. To analysis couples data in student mental dental around the years of Facebook's expansion with a generalized difference-in-differences empirical our. We find that the roll-out of Facebook at a college raised symptoms of poor mental health, especially misery. We also find that, among students predicted to be most susceptible to mental illness, the introducing of Facebook lights go increased utilization of mental healthcare services. Lastly, we locate the, after the introduction for Facebook, students were read likely to report experiences impairments to academic performance resulting from poor mental condition. Additional exhibits up mechanisms suggests that the results are due in Facebook fostering low social comparisons.

This data reseach for study of the digital behavior on social media and how is affecting mental health

We provide two datasets extracted from Twitter, in Spanish and English, and annotate each one with approximately 1,500 users who have been diagnosed with one of nine different mental disorders (ADHD, Autism, Anxiety, Bipolar, Depression, Eating disoders, OCD, PTSD and Schizophrenia) along with 1,700 matched-control users. For both datasets, the outcome is a total of just over 3,000 Twitter users with their corresponding timelines (the texts retrieved from each user cover at least 3 months of activity on the social media), which support two user-level classification tasks, binary and multiclass. — Dataset usage terms : By using this dataset, you agree to (i) use the content of this dataset and the data generated from the content of this dataset for non-commercial research only, and (ii) remain in compliance with Twitter's Developer Policy.

The author Jonathan Glazzard has written the book Social media and mental health in schools which was published in 2018 by Critical Publishing. The book has an ISBN of 1912508174/1912508181/1912508198 and is in the English language. The book is about Students-Mental health, Social media-Psychological aspects, Social media in education, Students-Mental health services.

Mental illness in adolescence has increased dramatically over the past few decades. In 2022, 22% of young people aged 17-24 had a probable mental disorder, an increase from 1 in 10 in 2017 to 1 in 6 in 2020 (NHS Digital, 2022). This has occurred alongside a rapid upsurge in the usage of social media by adolescents; 16-to 24-year-olds in the UK have the highest prevalence of social media user with 51% reporting that they spend too much time online (Ofcom, 2023). Such trends have sparked an explosion of research into the effects of social media usage on adolescent mental health, but a recent government inquiry concluded that there is a lack of high-quality research in this area. This reflects three primary limitations of existing research: an absence of longitudinal, causal studies, a lack of consideration for how (rather than how much) adolescents use social media, and little research on individual differences in adolescent vulnerability (Prinstein et al., 2020). The few studies that have employed (quasi)experimental methods to assess causality report increased psychological wellbeing (e.g., reduced depression) following the deactivation of social media accounts (Allcott et al., 2020), or an increase in adolescents’ depressive symptoms following the introduction of Facebook (Braghieri et al., 2022). This focus on usage frequency is overly simplistic, however, ignoring the range of behaviours exhibited on social media (Shaw et al., 2023). In the current study, we will identify how distinct styles of social media usage (i.e., interactive, reactive, passive) are related to adolescent mood and mental wellbeing over a period of 3-days, and whether this is moderated by person-specific factors, such as trait depressive tendencies. Objective 1: Determine the effect of interactive, reactive, and passive social media usage on adolescent wellbeing. We have developed a task – the Social Networking Site Behaviour Task (SNSBT) which classifies distinct styles of social media usage: “interactive” users are most likely to ‘share’ others’ content, “reactive” users express a tendency to ‘like’ than ‘share’ others’ content, and “passive” users tended to scroll through rather than engaging with others’ content (Shaw et al., 2022). In a sample of ~230 participants, we will examine how these styles of usage are related to adolescent wellbeing through a measure of state (momentary) mood (I-PANAT-SF) over a period of three-days. We predict that more interactive usage will be related to greater psychological wellbeing compared to more reactive and passive usage styles due to bi-directional exchanges fostering a sense of social connectedness and social capital. Objective 2: Define person characteristics that influence distinct styles of social media usage and moderate their relationship with adolescent wellbeing. Before participants complete the SNSBT on Day 1, they will also complete a measure of trait depression (STAI/D). This will allow us to examine whether the effects of distinct styles of social media usage on state adolescent wellbeing (mood) are further moderated by participant’s stable depressive tendencies. We predict that participants with lower trait depression will engage in more interactive styles of social media usage compared with those with higher trait depression. Note: Both questionnaires employed in this study are designed for non-clinical research – they cannot be used for diagnostic purposes.

The author Claire Edwards has written the book Social media and mental health : handbook for parents and guardians which was published in 2018 by Trigger Press. The book has an ISBN of 1911246701/1911246718/1911246725 and is in the English language. The book is about Internet and children, Social media-Psychological aspects.

Mixed methods PhD project exploring i) how young people (aged 13-14) use social media; ii) prospective mental health outcomes for different social media user types; iii) the role of social connectedness (school, family and peer) in the relationship between social media use and adolescent mental health and well-being

The dataset contains a survey conducted on the Chai platform. A total of 5,260 responses were collected. The dataset contains anonymized user IDs, email addresses (partially obscured for privacy), gender, age range, and several questions related to the impact of Social Chat AI on mental health and social anxieties. The questions are rated on a numerical scale, likely indicating the level of agreement or impact. Additionally, there are columns for optional feedback for developers and timestamps for when the survey started and was submitted.

Background Recently, there is heated debate about social media’s effects on mental health. Whether social media is harmful or beneficial really depends on how we use it (Lee & Hancock, 2023). In fact, mental health interventions delivered through social media or the internet are more cost-effective, scalable, demand limited training, and acceptable. In today’s context of the national mental health crisis and severe lack of certified counselors, technology-enabled interventions bear much hope. Past studies either focused on social media interventions’ impact of physical health or the effects of social media abstinence on mental health.Given the recently growing body of research utilizing social media intervention to improve mental health, we are motivated to conduct this meta-analysis on the effectiveness of social-media or internet-based interventions on mental health improvement.

Research objectives RQ1: What are the overall impacts of social-media-based and internet-based randomized-controlled-trial programs on the reduction of negative mental health outcomes that inhibit well-being (depression, anxiety, stress, suicidal ideation, negative affect)? H1: We hypothesize that social-media-based and internet-based randomized-controlled-trial programs can effectively reduce negative mental health outcomes.

RQ2: What are the overall impacts of social-media-based and internet-based randomized-controlled-trial programs on the improvement of positive mental health outcomes (social support, well-being, positive affect, gratitude, life satisfaction, friendship satisfaction, happiness)? H2: We hypothesize that social-media-based and internet-based randomized-controlled-trial programs can effectively improve positive mental health outcomes.

RQ3: To what extent do intervention outcomes differ according to methodological criteria, such as sample size, program duration or number of sessions; program delivery, such as self-guided vs. guided, social media based vs. internet based vs. mobile based vs. AI based; program content: cognitive behavior therapy vs. others; study characteristics, such as publication year (2015-2020, 2020-2024), age, culture (collectivistic vs. individualistic), female percentage (more than 50% female vs. less than 50% female)? H3: We hypothesize that programs are more effective if they have a smaller sample, a longer duration or more sessions, are guided, involve cognitive behavior therapy, implemented among younger people, were published between 2020 and 2024, among participants from collectivistic culture, have more than 50% female, and are delivered over social media.

RQ4: In the follow-up assessments, does the effectiveness of the programs last? H4: We hypothesize that the effect will be smaller compared to post-test assessment results.

Literature Search To ensure comprehensive literature coverage, the first author conducted a meticulous search strategy encompassing database queries, handsearching, and backward citation tracking. Employing a predefined set of keywords on social media, intervention method, and mental health (social media OR Facebook OR Instagram OR WhatsApp OR Twitter OR Pinterest OR LinkedIn OR Reddit OR Line OR Wechat OR Youtube OR Discord OR KakaoTalk OR Telegram OR Snapchat OR Tiktok AND social support OR well-being OR help-seeking behavior OR mental health literacy OR attitude towards mental health OR depression OR anxiety OR stress OR mental health stigma AND intervention OR program OR workshop OR module OR course), the first author systematically scoured databases including the Education Resources Information Center, PsychINFO, Scopus, PsychArticles, Communication and Mass Media Complete, and Proquest. Supplementary to the database search, targeted handsearching was performed through Paperfetcher across select reputable journals specific to the field. Finally, forward citation tracking and backward citation chasing were performed using CitationChaser on relevant systematic reviews and meta-analyses. All literature searches were concluded by March 2023, yielding a total of 8944 studies, subsequently imported into Covidence for screening. Covidence, chosen for its functionality in facilitating full-text review and availability of free software licenses through the authors’ affiliated institutions, was utilized for both title/abstract screening and full-text review via a double-blinded approach. We will keep track of the proportionate agreement in the full-text screening phase and plan to resolve any conflicts through weekly group discussions. We plan to conduct double-coding in Google spreadsheet.

Eligibility criteria 1) Studies must use randomization with the presence of a control group. The level of random assignment may be schools, classes, or students, etc. 2) There must be at least 30 participants per experimental condition (Cheung & Slavin, 2016) 3) Difference between conditions at baseline on mental health measure must be less than 0.25 standard deviations (SDs). 4) Differential attrition between treatment and control groups must be less than 15%. 5) Intervention or instruction should be delivered by non-researchers. Treatments cannot be delivered by researchers, because effect sizes are inflated when researchers deliver the treatment (Scammacca et al., 2007). 6) Outcomes of interest measurements must include quantitative measures of mental health outcomes 7) Focus populations must be adults including and above 18 years old. This means that we exclude students or young adults since the interpretations are very different from developmental perspectives. 8) Text must be available on the Internet and written in English. 9) Articles must be published on or after January 1st 2005.

Analytical plan This study will conduct a meta-analysis using the R statistical software, specifically employing the metafor package for analysis (Viechtbauer, 2010). Weighted mean effect sizes and meta-analytic tests such as Q statistics will be utilized. Weights will be assigned to each study based on inverse variance (Lipsey & Wilson, 2001) and will be adjusted according to Hedges’ (2007) recommendations. A random-effects model in meta-regression will be employed due to the presence of a range of effect sizes dependent on various factors (Borenstein et al., 2010). For cluster randomization, adjustments will be made based on methods adapted from the What Works Clearinghouse (2020). The analysis will include 9 pairs of moderators and will examine differential effects by incorporating interaction terms. All moderators and covariates will be grand-mean centered to aid interpretation. Mean effect sizes will be derived from the metaregression model, which will account for potential moderators and covariates. To assess selection bias, we plan to adopt selection modeling instead of other traditional techniques (e.g., funnel plot, Egger’s regression, fail-safe N) because of the limitations in these traditional techniques. Selection modeling involves a model of the selection process that uses a weight function to estimate the probability of selection in random-effect meta-analysis (Hedges, 1992). Selection modeling is the most recommended method to investigate meta-analyses’ publication bias (Terrin et al., 2005). We plan to make the complete dataset and code publicly available on github. In terms of result presentation, we plan to use PRISMA flow graph, forest plot, EB plot and Meta-rain cloud plot to assist understanding. In terms of handling missing data, we will employ the method of “infer, initiate, impute” (Pigott & Polanin, 2020). First, we infer the required information if other information could inform a well-reasoned best estimate. Second, we inquire by trying to contact the corresponding authors when an effect size cannot be calculated or when moderator information is missing. This option works the best for recently published studies. Finally, if the authors fail to respond in time, We will use multiple imputations, such as maximum likelihood using the EM algorithm, to account for missing moderator data (e.g., missing racial demographics), as recommended by Pigott (2001). We plan to use the R package mice to conduct the imputations. In terms of model building, we plan to group our covariates into three categories: study-level characteristics, sample characteristics, and measurement characteristics. We will run a separate mixed-effects meta-regression model with each category of covariates. From these three models, we will select moderators with p-values less than .10 for inclusion into a combined final meta-regression model.

Moderators This study will add moderators to explain the difference in impacts based on Methodological criteria: sample size, program duration or number of sessions, clustered vs. not clustered Program delivery: self-guided vs. guided, social media based vs. internet based vs. mobile based vs. AI based Program content: cognitive behavior therapy vs. others Study characteristics: publication year (2015-2020, 2020-2024), age, culture (collectivistic vs. individualistic), female percentage (more than 50% female vs. less than 50% female)

References Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2010). A basic introduction to fixed-effect and random-effects models for meta-analysis. Research Synthesis Methods, 1(2), 97–111. https://doi.org/10.1002/jrsm.12 Cheung, A. C. K., & Slavin, R. E. (2016). How methodological features affect effect sizes in education. Educational Researcher, 45(5), 283–292. https://doi.org/10.3102/0013189X16656615 Hedges, L. (1992). Modeling selection effects in meta-analysis. Statistical Science, 7. https://doi.org/10.1214/ss/1177011364 Hedges, L. V. (2007). Effect sizes in cluster-randomized designs. Journal of Educational and Behavioral Statistics, 32(4), 341–370. https://doi.org/10.3102/1076998606298043 Lee, A. Y., & Hancock, J. T. (2023) Social media mindsets: A new approach to

No description was included in this Dataset collected from the OSF

This study investigates change in students’ social networks and mental health at the time of the COVID-19 crisis in April 2020. We surveyed multiple dimensions of social networks (pleasant interaction, friendship, social support, co-studying) and mental health indicators (depression, anxiety, stress, loneliness) before and during the crisis among Swiss undergraduate students (N=212). We find that interaction and co-studying networks had become sparser, and more students were studying alone. Furthermore, students’ levels of stress, anxiety, loneliness, and depressive symptoms got worse. Stressors shifted from fears of missing out on social life to worries about health, family, friends, and their future. Exploratory analyses suggest that COVID-19 specific worries, isolation in social networks, lack of interaction and emotional support, and physical isolation were associated with negative mental health trajectories. The results offer starting points to identify and support students at higher risk of social isolation and negative psychological effects during the COVID-19 pandemic.