This dataset contains posts from 28 subreddits (15 mental health support groups) from 2018-2020. We used this dataset to understand the impact of COVID-19 on mental health support groups from January to April, 2020 and included older timeframes to obtain baseline posts before COVID-19.

Please cite if you use this dataset:

Low, D. M., Rumker, L., Torous, J., Cecchi, G., Ghosh, S. S., & Talkar, T. (2020). Natural Language Processing Reveals Vulnerable Mental Health Support Groups and Heightened Health Anxiety on Reddit During COVID-19: Observational Study. Journal of medical Internet research, 22(10), e22635.

@article{low2020natural,
 title={Natural Language Processing Reveals Vulnerable Mental Health Support Groups and Heightened Health Anxiety on Reddit During COVID-19: Observational Study},
 author={Low, Daniel M and Rumker, Laurie and Torous, John and Cecchi, Guillermo and Ghosh, Satrajit S and Talkar, Tanya},
 journal={Journal of medical Internet research},
 volume={22},
 number={10},
 pages={e22635},
 year={2020},
 publisher={JMIR Publications Inc., Toronto, Canada}
}

License

This dataset is made available under the Public Domain Dedication and License v1.0 whose full text can be found at: http://www.opendatacommons.org/licenses/pddl/1.0/

It was downloaded using pushshift API. Re-use of this data is subject to Reddit API terms.

Reddit Mental Health Dataset

Contains posts and text features for the following timeframes from 28 mental health and non-mental health subreddits:

• 15 specific mental health support groups (r/EDAnonymous, r/addiction, r/alcoholism, r/adhd, r/anxiety, r/autism, r/bipolarreddit, r/bpd, r/depression, r/healthanxiety, r/lonely, r/ptsd, r/schizophrenia, r/socialanxiety, and r/suicidewatch)
• 2 broad mental health subreddits (r/mentalhealth, r/COVID19_support)
• 11 non-mental health subreddits (r/conspiracy, r/divorce, r/fitness, r/guns, r/jokes, r/legaladvice, r/meditation, r/parenting, r/personalfinance, r/relationships, r/teaching).

filenames and corresponding timeframes:

• post: Jan 1 to April 20, 2020 (called "mid-pandemic" in manuscript; r/COVID19_support appears). Unique users: 320,364.
• pre: Dec 2018 to Dec 2019. A full year which provides more data for a baseline of Reddit posts. Unique users: 327,289.
• 2019: Jan 1 to April 20, 2019 (r/EDAnonymous appears). A control for seasonal fluctuations to match post data. Unique users: 282,560.
• 2018: Jan 1 to April 20, 2018. A control for seasonal fluctuations to match post data. Unique users: 177,089

Unique users across all time windows (pre and 2019 overlap): 826,961.

See manuscript Supplementary Materials (https://doi.org/10.31234/osf.io/xvwcy) for more information.

Note: if subsampling (e.g., to balance subreddits), we recommend bootstrapping analyses for unbiased results.

This database is comprised of 951 participants who provided self-report data online in their school classrooms. The data was collected in 2016 and 2017. The dataset is comprised of 509 males (54%) and 442 females (46%). Their ages ranged from 12 to 16 years (M = 13.69, SD = 0.72). Seven participants did not report their age. The majority were born in Australia (N = 849, 89%). The next most common countries of birth were China (N = 24, 2.5%), the UK (N = 23, 2.4%), and the USA (N = 9, 0.9%). Data were drawn from students at five Australian independent secondary schools. The data contains item responses for the Spence Children’s Anxiety Scale (SCAS; Spence, 1998) which is comprised of 44 items. The Social media question asked about frequency of use with the question “How often do you use social media?”. The response options ranged from constantly to once a week or less. Items measuring Fear of Missing Out were included and incorporated the following five questions based on the APS Stress and Wellbeing in Australia Survey (APS, 2015). These were “When I have a good time it is important for me to share the details online; I am afraid that I will miss out on something if I don’t stay connected to my online social networks; I feel worried and uncomfortable when I can’t access my social media accounts; I find it difficult to relax or sleep after spending time on social networking sites; I feel my brain burnout with the constant connectivity of social media. Internal consistency for this measure was α = .81. Self compassion was measured using the 12-item short-form of the Self-Compassion Scale (SCS-SF; Raes et al., 2011). 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. References: 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 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 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

According to a survey conducted in June 2024 in the United States, 52 percent of respondents aged 65 years and over thought that social media platforms definitely should be required to display cigarette-style health messages on them, warning users of their association with mental health harm for adolescents. Overall, 44 percent of respondents aged between 45 and 64 years felt the same. In addition, 36 percent of respondents aged between 18 and 29 years thought that social networks definitely should be required to display cigarette-style health messages.

As of July 2024, approximately 58 percent of parents in the United States reported using cell phones with built-in tracking to monitor their children. Family monitoring apps were the second most-used solution among U.S. parents, as 53 percent of respondents reported using this method to track their children's location.

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.

During the first quarter of 2025, approximately 21.4 million TikTok accounts were removed from the platform due to suspicion of being operated by users under the age of 13. During the last measured period, around 73.1 million fake accounts were removed from fake accounts removed from TikTok.

This dataset is structured as a graph, where nodes represent users and edges capture their interactions, including tweets, retweets, replies, and mentions. Each node provides detailed user attributes, such as unique ID, follower and following counts, and verification status, offering insights into each user's identity, role, and influence in the mental health discourse. The edges illustrate user interactions, highlighting engagement patterns and types of content that drive responses, such as tweet impressions. This interconnected structure enables sentiment analysis and public reaction studies, allowing researchers to explore engagement trends and identify the mental health topics that resonate most with users.

The dataset consists of three files: 1. Edges Data: Contains graph data essential for social network analysis, including fields for UserID (Source), UserID (Destination), Post/Tweet ID, and Date of Relationship. This file enables analysis of user connections without including tweet content, maintaining compliance with Twitter/X’s data-sharing policies. 2. Nodes Data: Offers user-specific details relevant to network analysis, including UserID, Account Creation Date, Follower and Following counts, Verified Status, and Date Joined Twitter. This file allows researchers to examine user behavior (e.g., identifying influential users or spam-like accounts) without direct reference to tweet content. 3. Twitter/X Content Data: This file contains only the raw tweet text as a single-column dataset, without associated user identifiers or metadata. By isolating the text, we ensure alignment with anonymization standards observed in similar published datasets, safeguarding user privacy in compliance with Twitter/X's data guidelines. This content is crucial for addressing the research focus on mental health discourse in social media. (References to prior Data in Brief publications involving Twitter/X data informed the dataset's structure.)

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

B-MHD (Bengali Mental Health Disorder Text Dataset)B-MHD is a specialized dataset containing 7,131 manually annotated Bangla social media texts, designed to facilitate the detection of mental health disorder-related content. Curated from platforms such as Facebook, YouTube, Twitter, and Reddit, the dataset includes texts labeled for the presence or absence of mental health indicators.

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, while 12 percent of users said it had a very negative impact. Furthermore, 22 percent of respondents said social media had a somewhat negative effect on their mental health. Is social media addictive? A 2023 survey of individuals between 11 and 59 years old in the United States found that over 73 percent of TikTok users agreed that the platform was addictive. Furthermore, nearly 27 percent of those surveyed reported experiencing negative psychological effects related to TikTok use. Users belonging to Generation Z were the most likely to say that TikTok is addictive, yet millennials felt the negative effects of using the app more so than Gen Z. In the U.S., it is also not uncommon for social media users to take breaks from using online platforms, and as of March 2024, over a third of adults in the country had done so. Following mental health-related content Although online users may be aware of the negative and addictive aspects of social media, it is also a useful tool for finding supportive content. In a global survey conducted in 2023, 32 percent of social media users followed therapists and mental health professionals on social media. Overall, 24 percent of respondents said that they followed people on social media if they had the same condition as they did. Between January 2020 and March 2023, British actress and model Cara Delevingne was the celebrity mental health activist with the highest growth in searches tying her name to the topic.

According to a survey conducted in the United States in March 2024, 45 percent of social media users aged between 18 and 29 years had taken an extended mental health break from social media. Additionally, 42 percent users aged 30 to 44 years had done the same. Respondents aged 65 years and over were the least likely to have taken a break.

As of July 2024, approximately 22 percent of parents in the United States reported that they had caught their children online engaging in activities they disapproved of, using online tracking methods. In comparison, 24 percent of parents said they had caught their children in both online and offline situations doing things they should not have been involved in.

This dataset contains 5,000 records of individuals' mental health conditions and lifestyle habits. The data includes demographic details, mental health consultation history, sleep patterns, stress levels, and social media usage. It is designed for sentiment analysis and mental health prediction research.

The daily aggregated time-series data used in this study, "Predicting public mental health needs in a crisis using social media indicators: A Singapore big data study" (including actual values and normalised values), are available in the figshare repository. The count of daily emergency room visits data (“IMH Visits”) is available from the corresponding author upon reasonable request.The study can be cited as:Othman, N.A., Panchapakesan, C., Loh, S.B., Zhang, M., Gupta, R.K., Martanto, W., Phang, Y.S., Morris, R.J.T., Loke, W.C., Tan, K.B., Subramaniam, M., Yang, Y. Predicting public mental health needs in a crisis using social media indicators: a Singapore big data study. Sci Rep 14, 23222 (2024). https://doi.org/10.1038/s41598-024-73978-5

In a survey conducted in 2023, 32 percent of social media users followed therapists and mental health professionals for mental health content. One quarter of respondents said that they followed people on social media if they had the same condition as they did. Furthermore, 20 percent of social network users followed mental health advocates and brands for mental health content.

This dataset contains 1,000 anonymized social media posts labeled for signs of mental health conditions including depression, anxiety, suicidal thoughts, or no apparent issues. It is designed to help researchers and developers build early warning systems, mental health support chatbots, or other AI tools to detect and assist individuals experiencing mental health challenges. The posts are synthetic but modeled to reflect realistic language and emotional cues commonly found on social media.

According to an online survey conducted in January 2023, 55 percent of adults in the United States reported being very concerned about the impact of social media on children's mental health. Overall, almost one-third of U.S. adults stated they were somewhat concerned. Additionally, just six percent of adults said they were not at all worried about the effect that social media has on kids' mental health.

Our dataset, BSMDD, was collected from various open social media platforms and translated and annotated by native Bengali speakers with expertise in both language and mental health. It contains 21,910 cleaned samples, including 10,961 labeled as Depressed and 10,949 as Non-Depressed. The dataset is publicly accessible, providing a valuable resource for further research in depression detection in Bengali social media content. The expert annotation process, conducted by professionals, ensures high validity, making BSMDD particularly important for advancing mental health research through social media analysis.

According to a survey conducted in England in 2021, **** 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 **** percent of respondents with probable mental disorder agreed that they spent more time on social media then they meant to.