According to a 2023 survey conducted in the United States, teenagers spent an average of 4.8 hours every day on social media platforms. Girls spent 5.3 hours on social networks daily, compared to 4.4 hours for boys. YouTube and TikTok were the most popular online networks among those aged 13 to 19, with 1.9 and 1.5 hours of average daily engagement, respectively. The most used platform for girls was TikTok, while the most used platform for boys was YouTube. Are teens constantly connected to social media? YouTube, TikTok, Instagram and Snapchat are the most attractive and time-consuming platforms for young internet users. A survey conducted in the U.S. in 2023 found that 62 percent of teenagers were almost constantly connected to Instagram, and 17 percent were almost constantly connected to TikTok. Overall, 71 percent of teens used YouTube daily, and 47 percent used Snapchat daily. Furthermore, YouTube had a 93 percent reach among American teens in 2023, down from 95 percent in 2022. Teens and their internet devices For younger generations especially, social media is mostly accessed via mobile devices, and almost all teenagers in the United States have smartphone access. A 2023 survey conducted in the U.S. found that 92 percent of teens aged 13 to 14 years had access to a smartphone at home, as well as 97 percent of those aged 15 to 17. Additionally, U.S. girls were slightly more likely than their male counterparts to have access to a smartphone.

In 2024, children in the United Kingdom spent an average of *** minutes per day on TikTok. This was followed by Instagram, as children in the UK reported using the app for an average of ** minutes daily. Children in the UK aged between four and 18 years also used Facebook for ** minutes a day on average in the measured period. Mobile ownership and usage among UK children In 2021, around ** percent of kids aged between eight and 11 years in the UK owned a smartphone, while children aged between five and seven having access to their own device were approximately ** percent. Mobile phones were also the second most popular devices used to access the web by children aged between eight and 11 years, as tablet computers were still the most popular option for users aged between three and 11 years. Children were not immune to the popularity acquired by short video format content in 2020 and 2021, spending an average of ** minutes per day engaging with TikTok, as well as over ** minutes on the YouTube app in 2021. Children data protection In 2021, ** percent of U.S. parents and ** percent of UK parents reported being slightly concerned with their children’s device usage habits. While the share of parents reporting to be very or extremely concerned was considerably smaller, children are considered among the most vulnerable digital audiences and need additional attention when it comes to data and privacy protection. According to a study conducted during the first quarter of 2022, ** percent of children’s apps hosted in the Google Play Store and ** percent of apps hosted in the Apple App Store transmitted users’ locations to advertisers. Additionally, ** percent of kids’ apps were found to collect persistent identifiers, such as users’ IP addresses, which could potentially lead to Children’s Online Privacy Protection Act (COPPA) violations in the United States. In the United Kingdom, companies have to take into account several obligations when considering online environments for children, including an age-appropriate design and avoiding sharing children’s data.

This dataset was originally collected for a data science and machine learning project that aimed at investigating the potential correlation between the amount of time an individual spends on social media and the impact it has on their mental health.

The project involves conducting a survey to collect data, organizing the data, and using machine learning techniques to create a predictive model that can determine whether a person should seek professional help based on their answers to the survey questions.

This project was completed as part of a Statistics course at a university, and the team is currently in the process of writing a report and completing a paper that summarizes and discusses the findings in relation to other research on the topic.

The following is the Google Colab link to the project, done on Jupyter Notebook -

https://colab.research.google.com/drive/1p7P6lL1QUw1TtyUD1odNR4M6TVJK7IYN

The following is the GitHub Repository of the project -

https://github.com/daerkns/social-media-and-mental-health

Libraries used for the Project -

Pandas
Numpy
Matplotlib
Seaborn
Sci-kit Learn

How much time do people spend on social media?

              As of 2024, the average daily social media usage of internet users worldwide amounted to 143 minutes per day, down from 151 minutes in the previous year. Currently, the country with the most time spent on social media per day is Brazil, with online users spending an average of three hours and 49 minutes on social media each day. In comparison, the daily time spent with social media in
              the U.S. was just two hours and 16 minutes. Global social media usageCurrently, the global social network penetration rate is 62.3 percent. Northern Europe had an 81.7 percent social media penetration rate, topping the ranking of global social media usage by region. Eastern and Middle Africa closed the ranking with 10.1 and 9.6 percent usage reach, respectively.
              People access social media for a variety of reasons. Users like to find funny or entertaining content and enjoy sharing photos and videos with friends, but mainly use social media to stay in touch with current events friends. Global impact of social mediaSocial media has a wide-reaching and significant impact on not only online activities but also offline behavior and life in general.
              During a global online user survey in February 2019, a significant share of respondents stated that social media had increased their access to information, ease of communication, and freedom of expression. On the flip side, respondents also felt that social media had worsened their personal privacy, increased a polarization in politics and heightened everyday distractions.

As of February 2025, the average daily social media usage of internet users worldwide amounted to 141 minutes per day, down from 143 minutes in the previous year. Currently, the country with the most time spent on social media per day is Brazil, with online users spending an average of 3 hours and 49 minutes on social media each day. In comparison, the daily time spent with social media in the U.S. was just 2 hours and 16 minutes. Global social media usage Currently, the global social network penetration rate is 62.3 percent. Northern Europe had an 81.7 percent social media penetration rate, topping the ranking of global social media usage by region. Eastern and Middle Africa closed the ranking with 10.1 and 9.6 percent usage reach, respectively. People access social media for a variety of reasons. Users like to find funny or entertaining content and enjoy sharing photos and videos with friends, but mainly use social media to stay in touch with current events and friends. Global impact of social media Social media has a wide-reaching and significant impact on not only online activities but also offline behavior and life in general. During a global online user survey in February 2019, a significant share of respondents stated that social media had increased their access to information, ease of communication, and freedom of expression. On the flip side, respondents also felt that social media had worsened their personal privacy, increased polarization in politics, and heightened everyday distractions.

Introduction: Social media has become an integrated part of daily life, with an estimated 3 billion social media users worldwide. Adolescents and young adults are the most active users of social media. Research on social media has grown rapidly, with the potential association of social media use and mental health and well-being becoming a polarized and much-studied subject. The current body of knowledge on this theme is complex and difficult-to-follow. The current paper presents a scoping review of the published literature in the research field of social media use and its association with mental health and well-being among adolescents.Methods and Analysis: First, relevant databases were searched for eligible studies with a vast range of relevant search terms for social media use and mental health and well-being over the past five years. Identified studies were screened thoroughly and included or excluded based on prior established criteria. Data from the included studies were extracted and summarized according to the previously published study protocol.Results: Among the 79 studies that met our inclusion criteria, the vast majority (94%) were quantitative, with a cross-sectional design (57%) being the most common study design. Several studies focused on different aspects of mental health, with depression (29%) being the most studied aspect. Almost half of the included studies focused on use of non-specified social network sites (43%). Of specified social media, Facebook (39%) was the most studied social network site. The most used approach to measuring social media use was frequency and duration (56%). Participants of both genders were included in most studies (92%) but seldom examined as an explanatory variable. 77% of the included studies had social media use as the independent variable.Conclusion: The findings from the current scoping review revealed that about 3/4 of the included studies focused on social media and some aspect of pathology. Focus on the potential association between social media use and positive outcomes seems to be rarer in the current literature. Amongst the included studies, few separated between different forms of (inter)actions on social media, which are likely to be differentially associated with mental health and well-being outcomes.

Dataset Description:

This dataset captures the real-world online behavior of teenagers, focusing on e-safety awareness, cybersecurity risks, and device interactions. The data was collected from network activity logs and e-safety monitoring systems across various educational institutions and households in Texas and California. Spanning from January 2017 to October 2024, this dataset includes interactions with social media platforms, educational websites, and other online services, providing an in-depth look at teenage online activities in urban and suburban settings. The dataset is anonymized to protect user privacy and contains real incidents of network threats, security breaches, and cybersecurity behavior patterns observed in teenagers.

Use Cases:

Predicting e-safety awareness and online behavior patterns. Detecting malware exposure risk and cybersecurity vulnerabilities. Analyzing online habits related to social media and internet consumption. Evaluating cybersecurity behaviors like password strength, VPN usage, and phishing attempts. Features Overview:

S.No Feature Name Description 1 Device Type The type of device used during the online session (Mobile, Laptop, Tablet, Desktop, etc.) 2 Malware Detection Whether malware was detected on the device during the session (Yes/No) 3 Phishing Attempts Number of phishing attempts experienced during online activity 4 Social Media Usage Frequency of social media usage (Low, Medium, High) 5 VPN Usage Whether a VPN was used during the session (Yes/No) 6 Cyberbullying Reports Number of reported cyberbullying incidents 7 Parental Control Alerts Number of alerts triggered by parental control software 8 Firewall Logs Number of blocked or allowed network connections by the firewall 9 Login Attempts Number of login attempts during the session 10 Download Risk Risk level associated with downloaded files (Low, Medium, High) 11 Password Strength Strength of the passwords used (Weak, Moderate, Strong) 12 Data Breach Notifications Number of alerts regarding compromised personal information 13 Online Purchase Risk Risk level of online purchases made (Low, Medium, High) 14 Education Content Usage Frequency of engagement with educational content (Low, Medium, High) 15 Age Group Age category of the teenager (Under 13, 13-16, 17-19) 16 Geolocation Location of network access (US, EU, etc.) 17 Public Network Usage Whether the online activity occurred over a public network (Yes/No) 18 Network Type Type of network connection (WiFi, Cellular, etc.) 19 Hours Online Total hours spent online during the session 20 Website Visits Number of websites visited per hour during the session 21 Peer Interactions Level of peer-to-peer interactions during online activity 22 Risky Website Visits Whether visits to risky websites occurred (Yes/No) 23 Cloud Service Usage Whether cloud services were accessed during the session (Yes/No) 24 Unencrypted Traffic Whether unencrypted network traffic was accessed during the session (Yes/No) 25 Ad Clicks Whether online advertisements were clicked during the session (Yes/No) 26 Insecure Login Attempts Number of insecure login attempts made (e.g., over unencrypted networks) Potential Research and Machine Learning Applications:

Cybersecurity and anomaly detection models. Predictive modeling for e-safety awareness and risk behaviors. Time-series analysis of internet consumption and security threat trends. Behavioral clustering and pattern recognition in teenage online activity. Data Collection Method: The data was collected through collaboration with local schools and cybersecurity monitoring agencies. Real-time network monitoring systems captured interactions across different online platforms. All personally identifiable information (PII) was anonymized to ensure privacy, making the dataset ideal for public use in research and machine learning tasks.

This dataset provides a rich foundation for studying teenage online behavior patterns and developing predictive models for cybersecurity awareness and risk mitigation. Researchers and data scientists can use this data to create models that better understand online behavior, identify security risks, and design interventions to improve e-safety for teenagers.

This database is comprised of 603 participants who provided self-report data online in their school classrooms. The data was collected in 2016 and 2017. The dataset is comprised of 208 males (34%) and 395 females (66%). Their ages ranged from 12 to 15 years. Their age in years at baseline is provided. The majority were born in Australia. Data were drawn from students at two Australian independent secondary schools. The data contains total responses for the following scales: The Intolerance of Uncertainty Scale (IUS-12; Short form; Carleton et al, 2007) is a 12-item scale measuring two dimensions of Prospective and Inhibitory intolerance of uncertainty. Two subscales of the Children’s Automatic Thoughts Scale (CATS; Schniering & Rapee, 2002) were administered. The Peronalising and Social Threat were each composed of 10 items. UPPS Impulsive Behaviour Scale (Whiteside & Lynam, 2001) which is comprised of 12 items. Dispositional Envy Scale (DES; Smith et al, 1999) which is comprised of 8 items. Spence Children’s Anxiety Scale (SCAS; Spence, 1998) which is comprised of 44 items. Three subscales totals included were the GAD subscale (labelled SCAS_GAD), the OCD subscale (labelled SCAS_OCD) and the Social Anxiety subscale (labelled SCAS_SA). Each subscale was comprised of 6 items. Avoidance and Fusion Questionnaire for Youth (AFQ-Y; Greco et al., 2008) which is comprised of 17 items. Distress Disclosure Index (DDI; Kahn & Hessling, 2001) which is comprised of 12 items. Repetitive Thinking Questionnaire-10 (RTQ-10; McEvoy et al., 2014) which is comprised of 10 items. The Brief Fear of Negative Evaluation Scale, Straightforward Items (BFNE-S; Rodebaugh et al., 2004) which is comprised of 8 items. Short Mood and Feelings Questionnaire (SMFQ; Angold et al., 1995) which is comprised by 13 items. The Self-Compassion Scale Short Form (SCS-SF; Raes et al., 2011) which is comprised by 12 items. The subscales include Self Kindness, Self Judgment, Social Media subscales - These subscale scores were based on social media questions composed for this project and also drawn from three separate scales as indicated in the table below. The original scales assessed whether participants experience discomfort and a fear of missing out when disconnected from social media (taken from the Australian Psychological Society Stress and Wellbeing Survey; Australian Psychological Society, 2015a), style of social media use (Tandoc et al., 2015b) and Fear of Missing Out (Przybylski et al., 2013c). The items in each subscale are listed below. Pub_Share Public Sharing When I have a good time it is important for me to share the details onlinec On social media how often do you write a status updateb On social media how often do you post photosb Surveillance_SM On social media how often do you read the newsfeed On social media how often do you read a friend’s status updateb On social media how often do you view a friend’s photob On social media how often do you browse a friend’s timelineb Upset Share On social media how often do you go online to share things that have upset you? Text private On social media how often do you Text friends privately to share things that have upset you? Insight_SM Social Media Reduction I use social media less now because it often made me feel inadequate FOMO I am afraid that I will miss out on something if I don’t stay connected to my online social networksa. I feel worried and uncomfortable when I can’t access my social media accountsa. Neg Eff of SM I find it difficult to relax or sleep after spending time on social networking sitesa. I feel my brain ‘burnout’ with the constant connectivity of social mediaa. I notice I feel envy when I use social media.
I can easily detach from the envy that appears following the use of social media (reverse scored) DES_SM Envy Mean acts online Feeling envious about another person has led me to post a comment online about another person to make them laugh Feeling envious has led me to post a photo online without someone’s permission to make them angry or to make fun of them Feeling envious has prompted me to keep another student out of things on purpose, excluding her from my group of friends or ignoring them. Substance Use: Two items measuring peer influence on alcohol consumption were adapted from the SHAHRP “Patterns of Alcohol Use” measure (McBride, Farringdon & Midford, 2000). These items were “When I am with friends I am quite likely to drink too much alcohol” and “Substances (alcohol, drugs, medication) are the immediate way I respond to my thoughts about a situation when I feel distressed or upset. Angold, A., Costello, E. J., Messer, S. C., & Pickles, A. (1995). Development of a short questionnaire for use in epidemiological studies of depression in children and adolescents. International Journal of Methods in Psychiatric Research, 5(4), 237–249. Australian Psychological Society. (2015). Stress and wellbeing in Australia survey. https://www.headsup.org.au/docs/default-source/default-document-library/stress-and-wellbeing-in-australia-report.pdf?sfvrsn=7f08274d_4 Greco, L.A., Lambert, W. & Baer., R.A. (2008) Psychological inflexibility in childhood and adolescence: Development and evaluation of the Avoidance and Fusion Questionnaire for Youth. Psychological Assessment, 20, 93-102. https://doi.org/10.1037/1040-3590.20.2.9 Kahn, J. H., & Hessling, R. M. (2001). Measuring the tendency to conceal versus disclose psychological distress. Journal of Social and Clinical Psychology, 20(1), 41–65. https://doi.org/10.1521/jscp.20.1.41.22254 McBride, N., Farringdon, F. & Midford, R. (2000) What harms do young Australians experience in alcohol use situations. Australian and New Zealand Journal of Public Health, 24, 54–60 https://doi.org/10.1111/j.1467-842x.2000.tb00723.x McEvoy, P.M., Thibodeau, M.A., Asmundson, G.J.G. (2014) Trait Repetitive Negative Thinking: A brief transdiagnostic assessment. Journal of Experimental Psychopathology, 5, 1-17. Doi. 10.5127/jep.037813 Przybylski, A. K., Murayama, K., DeHaan, C. R., & Gladwell, V. (2013). Motivational, emotional, and behavioral correlates of fear of missing out. Computers in human behavior, 29(4), 1841-1848. https://doi.org/10.1016/j.chb.2013.02.014 Raes, F., Pommier, E., Neff, K. D., & Van Gucht, D. (2011). Construction and factorial validation of a short form of the self-compassion scale. Clinical Psychology and Psychotherapy, 18(3), 250-255. https://doi.org/10.1002/cpp.702 Rodebaugh, T. L., Woods, C. M., Thissen, D. M., Heimberg, R. G., Chambless, D. L., & Rapee, R. M. (2004). More information from fewer questions: the factor structure and item properties of the original and brief fear of negative evaluation scale. Psychological assessment, 16(2), 169. https://doi.org/10.1037/10403590.16.2.169 Schniering, C. A., & Rapee, R. M. (2002). Development and validation of a measure of children’s automatic thoughts: the children’s automatic thoughts scale. Behaviour Research and Therapy, 40(9), 1091-1109. . https://doi.org/10.1016/S0005-7967(02)00022-0 Smith, R. H., Parrott, W. G., Diener, E. F., Hoyle, R. H., & Kim, S. H. (1999). Dispositional envy. Personality and Social Psychology Bulletin, 25(8), 1007-1020. https://doi.org/10.1177/01461672992511008 Spence, S. H. (1998). A measure of anxiety symptoms among children. Behaviour Research and Therapy, 36(5), 545-566. https://doi.org/10.1016/S0005-7967(98)00034-5 Tandoc, E. C., Ferrucci, P., & Duffy, M. (2015). Facebook use, envy, and depression among college students: Is facebooking depressing? Computers in Human Behavior, 43, 139–146. https://doi.org/10.1016/j.chb.2014.10.053 Whiteside, S.P. & Lynam, D.R. (2001) The five factor model and impulsivity: using a structural model of personality to understand impulsivity. Personality and Individual Differences 30,669-689. https://doi.org/10.1016/S0191-8869(00)00064-7 The data was collected by Dr Danielle A Einstein, Dr Madeleine Fraser, Dr Anne McMaugh, Prof Peter McEvoy, Prof Ron Rapee, Assoc/Prof Maree Abbott, Prof Warren Mansell and Dr Eyal Karin as part of the Insights Project. The data set has the option of downloading an excel file (composed of two worksheet tabs) or CSV files 1) Data and 2) Variable labels.

This dataset is designed to analyze mental health patterns in teenagers, focusing on stress levels using anonymized data from social media activity, surveys, and wearable devices. It consists of 5000 entries and 11 columns, each capturing different aspects of the user's daily behavior and well-being. The goal is to detect correlations between factors like social media usage, physical activity, sleep patterns, and stress levels. This dataset can be useful for research on adolescent mental health, early stress detection, and preventive care.

At 2:13 a.m., Ellie turned off her phone after endlessly scrolling through Instagram Reels. This wasn’t the first time she traded sleep for screen time, and like millions of others, she would wake up tired, groggy, and mentally foggy. In 2025, this silent epidemic of sleep disruption linked to social...

This survey charted the gambling, social media usage and subjective well-being of young people aged 15-25 years in the United States. The study was conducted as part of the "Problem Gambling and Social Media: Social Psychological Study on Youth Behaviour in Online Gaming Communities" research project. The aim of the project was to analyse how young social media users evaluate, adopt and share gambling-related online content and how online group processes affect their gambling and gambling-related attitudes. FSD's holdings also include two other datasets that were collected using a nearly identical questionnaire (FSD3399 and FSD3400). Data for the research project have been collected in Finland, the United States, Spain, and South Korea. First, the respondents were asked which social media services they used (e.g. Facebook, YouTube, Instagram, discussion forums, online casinos) and how often. Topics that the respondents discussed on gambling-related social media were charted more closely, and they were asked, for example, whether the discussion usually related to instructions or tips on gambling or to problem gambling and recovering from problem gambling. Some questions on the respondents' social media activity were also presented, for instance, how often they saw gambling-related advertising online, how often they changed their most important social media passwords, and how often they uploaded pictures of themselves on social media. The respondents were asked whether they had ever been harassed online or had been the victim of a crime on the Internet in the past three years (e.g. defamation, identity theft, fraud, sexual harassment). The respondents' identity bubbles on social media were surveyed by using the IBR scale (Identity Bubble Reinforcement Scale). The respondents were asked, for instance, whether they thought they could be themselves on social media and whether they only interacted with people similar to them on social media. Additionally, the CIUS scale (Compulsive Internet Use) was used to examine problems related to Internet use. Questions focused on, for example, whether the respondents found it difficult to stop using the Internet when they were online, whether people close to them said they should use the Internet less, and whether they felt restless, frustrated or irritated when they couldn't use the Internet. In the next section of the questionnaire, the respondents were randomly assigned to two groups for a vignette experiment. Respondents in the test group were told they belong to Group C because they had answered the earlier questions in a similar manner to others in the group. Those in the control group were given no information on the group. The respondents were presented with different gambling-related social media scenarios, and they were asked to evaluate the contents of the gambling-related messages by "liking" or "disliking" the message or by not reacting to it at all. Each respondent was shown four different gambling messages with different contents. Three factors were manipulated in the scenarios (2x2x2 design): expressed stance of the message on gambling (positive or negative), narrative perspective of the message (experience-driven first-person narration or fact-driven third-person narration) and majority opinion of other respondents on the message (positively or negatively biased distribution of likes or dislikes). For Group C, the majority opinion was seemingly provided by other Group C members, whereas for the control group the majority opinion was seemingly provided by other respondents. Additionally, the respondents' attitudes towards the message were surveyed with statements regarding, for instance, how likely they would find the message interesting or share it on social media. Next, the respondents' attitudes towards gambling were charted by using the ATGS scale (Attitudes Towards Gambling Scale). They were asked, for example, whether people should have the right to gamble whenever they want, whether most people who gamble do so sensibly and whether it would be better if gambling was banned altogether. The respondents' gambling habits were examined by using the SOGS scale (South Oaks Gambling Screen), and they were asked, for instance, which types of gambling they had done in the past 12 months (played slot machines, visited an online casino, bet on lotteries etc.), whether the people close to them had gambling problems, and whether they had borrowed money to gamble or to pay gambling debts. In addition, the respondents' alcohol consumption was surveyed with a few questions from the AUDITC scale (The Alcohol Use Disorders Identification Test), and they were asked whether they had used various drugs for recreational purposes (e.g. cannabis, LSD, amphetamine, opioids) and which online resources they had used to purchases these drugs (e.g. Facebook, Instagram, Craigslist). The respondents' subjective well-being and social relationships were examined next. The respondents were asked how happy they were in general and how satisfied they were with their economic situation and life in general. They were also asked how well the single statement "I have high self-esteem" from the SISE scale (Single-item Self-esteem Scale) described them. The three statements on lacking companionship, feeling left out and feeling isolated from the LONE scale (Three-item Loneliness Scale) were also included in the survey. Feelings of belonging to different groups or communities (e.g. family, friends, neighbourhood, parish/religious community) were charted, and the 12-item GHQ scale (General Health Questionnaire) was used to survey the respondents' recent mental health. Questions included, for example, whether the respondents had been able to concentrate on what they were doing, had felt they couldn't overcome their difficulties, and had been losing confidence in themselves. Finally, the respondents' sense of control over the events in their lives was examined with the MASTERY scale (Sense of Mastery Scale), with questions focusing on, for instance, whether they thought they had little control over the things that happen to them and whether they often felt helpless in dealing with the problems of life. The respondents' impulsivity was surveyed by using the EIS scale (Eysenck Impulsivity Scale) and their willingness to delay gratification was surveyed with the GRATIF scale (Delay of Gratification). Background variables included the respondent's gender, age, country of birth (own and parents') level of education, type of municipality of residence, household composition, disposable income, possible financial problems, and economic activity and occupational status.

IntroductionRising rates of adolescent overdose deaths attributed to counterfeit prescription drugs purchased using social media have drawn national attention to how these platforms might influence substance use. Research suggests a significant relationship exists between exposure to substance-related social media content and use of drugs and alcohol, but most studies are cross-sectional and limited by recall bias. This study used an ecological momentary assessment (EMA) protocol to collect longitudinal data on social media use and online drug-related exposures associated with youth substance use.MethodsParticipants, aged 12–23, receiving mental health treatment from a U.S. community-based hospital, joined a six-week, smartphone-based EMA protocol. Each day, participants completed a modified CRAFFT screen for daily substance use and a survey on substance-related online content exposure, and input data from their smartphone screen time reports. Analyses employed mixed effects logistic regression models to explore relationships between substance-related online exposures, substance and social media use.ResultsData was obtained from 25 youth, predominantly white non-Hispanic/Latinx (56.0%) and female (64.0%). Participants had significantly higher odds of substance use on days when exposed to substance-related digital content posted by peers (OR: 19.6). They were also more likely to report these exposures (OR: 7.7) and use substances (OR: 29.6) on days when Snapchat was one of their most frequently used smartphone applications.DiscussionOur results support existing concerns about specific social media platforms being potential mediators of youth substance use. Future EMA studies in larger cohorts should explore the role of social media platforms in substance procurement.

Through social media like Instagram, users are constantly exposed to “perfect” lives and bodies. Research in this field has predominantly focused on the mere time youth spend on Instagram and the effects on their body image, oftentimes uncovering negative effects. Little research has been done on the root of the influence: the consumed content itself. Hence, this study aims to qualitatively uncover the types of content that trigger youths’ body image. Using a diary study, 28 youth (Mage = 21.86; 79% female) reported 140 influential body image Instagram posts over five days, uncovering trigger points and providing their motivations, emotions, and impacts on body image. Based on these posts, four content categories were distinguished: Thin Ideal, Body Positivity, Fitness, and Lifestyle. These different content types triggered different emotions regarding body image, and clear gender distinctions in content could be noticed. The study increased youths’ awareness of Instagram's influence on their mood and body perception. The findings imply that the discussion about the effects of social media on body image should be nuanced, taking into account different types of content and users. Using this information, future interventions could focus on conscious use of social media rather than merely limiting its use.

Through social media like Instagram, users are constantly exposed to “perfect” lives and bodies. Research in this field has predominantly focused on the mere time youth spend on Instagram and the effects on their body image, oftentimes uncovering negative effects. Little research has been done on the root of the influence: the consumed content itself. Hence, this study aims to qualitatively uncover the types of content that trigger youths’ body image. Using a diary study, 28 youth (Mage = 21.86; 79% female) reported 140 influential body image Instagram posts over five days, uncovering trigger points and providing their motivations, emotions, and impacts on body image. Based on these posts, four content categories were distinguished: Thin Ideal, Body Positivity, Fitness, and Lifestyle. These different content types triggered different emotions regarding body image, and clear gender distinctions in content could be noticed. The study increased youths’ awareness of Instagram's influence on their mood and body perception. The findings imply that the discussion about the effects of social media on body image should be nuanced, taking into account different types of content and users. Using this information, future interventions could focus on conscious use of social media rather than merely limiting its use. Date Submitted: 2023-08-20

This project examined the role of technology use in teen dating violence and abuse, and bullying. The goal of the project was to expand knowledge about the types of abuse experiences youth have, the extent of victimization and perpetration via technology and new media (e.g., social networking sites, texting on cellular phones), and how the experience of such cyber abuse within teen dating relationships or through bullying relates to other life factors. This project carried out a multi-state study of teen dating violence and abuse, and bullying, the main component of which included a survey of youth from ten schools in five school districts in New Jersey, New York, and Pennsylvania, gathering information from 5,647 youth about their experiences. The study employed a cross-sectional, survey research design, collecting data via a paper-pencil survey. The survey targeted all youth who attended school on a single day and achieved an 84 percent response rate.

This dataset contains data from two studies investigating the relationship between social media use and self-esteem among Chinese adolescents, with a focus on the role of positive body image. Study 1 (Experiment): 152 adolescents aged 11–14 participated in a 2×2 between-subjects experiment examining the effects of viewing ideal-body vs. average-body images on self-esteem. Data includes pre- and post-exposure self-esteem scores, image condition, and demographics. Study 2 (Survey): 961 adolescents (Mage = 14.29) completed questionnaires assessing daily social media use, positive body image (BAS-2), and self-esteem (Rosenberg Scale). Data were used to test the mediating role of positive body image. Both studies were approved by the institutional ethics committee. Data are anonymized and suitable for secondary analysis.

This survey charted the gambling, social media usage and subjective well-being of young people aged 15-30 years in Finland. The study was conducted as part of the "Problem Gambling and Social Media: Social Psychological Study on Youth Behavior in Online Gaming Communities" research project. The aim of the project was to analyse how young social media users assess, adopt and share gambling-related online content and how online group processes affect their gambling and gambling-related attitudes. This dataset contains additional data collected from popular Finnish social media sites. FSD's holdings also include two other datasets that were collected using a nearly identical questionnaire (FSD3399 and FSD3591). Data for the research project have been collected in Finland, the United States, Spain, and South Korea. First, the respondents were asked which social media services they used (e.g. Facebook, YouTube, Instagram, discussion forums, online casinos) and how often. Topics that the respondents discussed on gambling-related social media were charted more closely, and they were asked, for example, whether the discussion usually related to instructions or tips on gambling or to problem gambling and recovering from problem gambling. Some questions on the respondents' social media activity were also presented, for instance, how often they saw gambling-related advertising online, how often they changed their most important social media passwords, and how often they uploaded pictures of themselves on social media. The respondents were asked whether they had ever been harassed online or had been the victim of a crime on the Internet in the past three years (e.g. defamation, identity theft, fraud, sexual harassment). The respondents' identity bubbles on social media were surveyed by using the IBR scale (Identity Bubble Reinforcement Scale). The respondents were asked, for instance, whether they thought they could be themselves on social media and whether they only interacted with people similar to them on social media. Additionally, the CIUS scale (Compulsive Internet Use) was used to examine problems related to Internet use. Questions focused on, for example, whether the respondents found it difficult to stop using the Internet when they were online, whether people close to them said they should use the Internet less, and whether they felt restless, frustrated or irritated when they couldn't use the Internet. In the next section of the questionnaire, the respondents were randomly assigned to two groups for a vignette experiment. Respondents in the test group were told they belong to Group C because they had answered the earlier questions in a similar manner to others in the group. Those in the control group were given no information on the group. The respondents were presented with different gambling-related social media scenarios, and they were asked to evaluate the contents of the gambling-related messages by "liking" or "disliking" the message or by not reacting to it at all. Each respondent was shown four different gambling messages with different contents. Three factors were manipulated in the scenarios (2x2x2 design): expressed stance of the message on gambling (positive or negative), narrative perspective of the message (experience-driven first-person narration or fact-driven third-person narration) and majority opinion of other respondents on the message (positively or negatively biased distribution of likes or dislikes). For Group C, the majority opinion was seemingly provided by other Group C members, whereas for the control group the majority opinion was seemingly provided by other respondents. Additionally, the respondents' attitudes towards the message were surveyed with statements regarding, for instance, how likely they would find the message interesting or share it on social media. Next, the respondents' attitudes towards gambling were charted by using the ATGS scale (Attitudes Towards Gambling Scale). They were asked, for example, whether people should have the right to gamble whenever they want, whether most people who gamble do so sensibly and whether it would be better if gambling was banned altogether. The respondents' gambling habits were examined by using the SOGS scale (South Oaks Gambling Screen), and they were asked, for instance, which types of gambling they had done in the past 12 months (played slot machines, visited an online casino, bet on lotteries etc.), whether the people close to them had gambling problems, and whether they had borrowed money to gamble or to pay gambling debts. In addition, the respondents' alcohol consumption was surveyed with a few questions from the AUDITC scale (The Alcohol Use Disorders Identification Test), and they were asked whether they had used various drugs for recreational purposes (e.g. cannabis, LSD, amphetamine, opioids). The respondents' subjective well-being and social relationships were examined next. The respondents were asked how happy they were in general and how satisfied they were with their financial circumstances and life in general. They were also asked how well the single statement "I have high self-esteem" from the SISE scale (Single-item Self-esteem Scale) described them. The three statements on lacking companionship, feeling left out and feeling isolated from the LONE scale (Three-item Loneliness Scale) were also included in the survey. Feelings of belonging to different groups or communities (e.g. family, friends, neighbourhood, parish/religious community) were charted, and the 12-item GHQ scale (General Health Questionnaire) was used to survey the respondents' recent mental health. Questions included, for example, whether the respondents had been able to concentrate on what they were doing, had felt they couldn't overcome their difficulties, and had been losing confidence in themselves. Finally, the respondents' sense of control over the events in their lives was examined with the MASTERY scale (Sense of Mastery Scale), with questions focusing on, for instance, whether they thought they had little control over the things that happen to them and whether they often felt helpless in dealing with the problems of life. The respondents' impulsivity was surveyed by using the EIS scale (Eysenck Impulsivity Scale) and their willingness to delay gratification was surveyed with the GRATIF scale (Delay of Gratification). Background variables included the respondent's gender, age, country of birth (own and parents') level of education, type of municipality of residence, number of inhabitants in municipality of residence, household composition, disposable income, possible financial problems, and economic activity and occupational status.

This data is on what and how undergraduate students of Covenant University, Nigeria use the social media. A survey was carried out among 2,798 undergraduate students. A close ended questionnaire was the instrument for data collection. These data indicate that they mainly use the social media for personal communication, information search, entertainment, academic as well as non-academic purposes. They use the social media for private messaging, socialization and sharing of ideas with their families, friends, colleagues and lecturers. These respondents are of the opinion that the paramount benefit they have derived from using the social media is freedom of expression as the social media provide the platforms for them to do so. The respondents spend at least an hour or two every day using the social media.

Media use assesses children's use of TV series, (computer) games and social media. In the YOUth Baby and Child cohort, questions focused on tablet use, watching TV series, the frequency of gaming and the frequency of reading. In the YOUth Child and Adolescent cohort, questions focused on gaming (type and frequency), social media use and instant messaging (type, frequency, number of followers/friends), and self-reported over-use of internet (e.g., at the expense of homework or sleep).

Data from: Sex-related Online Behaviors, Perceived Peer Norms and Adolescents’ Experience with Sexual Behavior: Testing an Integrative ModelSPSS file containing data from: Sex-related Online Behaviors, Perceived Peer Norms and Adolescents’ Experience with Sexual Behavior: Testing an Integrative ModelPLOS ONE data.savData from: Sex-related Online Behaviors, Perceived Peer Norms and Adolescents’ Experience with Sexual Behavior: Testing an Integrative ModelCVS file containing data from: Sex-related Online Behaviors, Perceived Peer Norms and Adolescents’ Experience with Sexual Behavior: Testing an Integrative ModelPLOS ONE data.csv