Natural language processing reveals vulnerable mental health support groups and heightened health anxiety on Reddit during COVID-19.
Why this work is in the frame
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Bibliographic record
Abstract
\n\n \n\nThis 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.\n\nPlease cite if you use this dataset:\n\nLow, 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.\n\n@article{low2020natural,\n title={Natural Language Processing Reveals Vulnerable Mental Health Support Groups and Heightened Health Anxiety on Reddit During COVID-19: Observational Study},\n author={Low, Daniel M and Rumker, Laurie and Torous, John and Cecchi, Guillermo and Ghosh, Satrajit S and Talkar, Tanya},\n journal={Journal of medical Internet research},\n volume={22},\n number={10},\n pages={e22635},\n year={2020},\n publisher={JMIR Publications Inc., Toronto, Canada}\n}\n\n\nLicense\n\nThis 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/\n\nIt was downloaded using pushshift API. Re-use of this data is subject to Reddit API terms.\n\n \n\nReddit Mental Health Dataset\n\nContains posts and text features for the following timeframes from 28 mental health and non-mental health subreddits:\n\n\n\t15 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)\n\t2 broad mental health subreddits (r/mentalhealth, r/COVID19_support)\n\t11 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).\n\n\nfilenames and corresponding timeframes:\n\n\n\tpost: Jan 1 to April 20, 2020 (called "mid-pandemic" in manuscript; r/COVID19_support appears). Unique users: 320,364. \n\tpre: Dec 2018 to Dec 2019. A full year which provides more data for a baseline of Reddit posts. Unique users: 327,289.\n\t2019: Jan 1 to April 20, 2019 (r/EDAnonymous appears). A control for seasonal fluctuations to match post data. Unique users: 282,560.\n\t2018: Jan 1 to April 20, 2018. A control for seasonal fluctuations to match post data. Unique users: 177,089\n\n\nUnique users across all time windows (pre and 2019 overlap): 826,961.\n\nSee manuscript Supplementary Materials (https://doi.org/10.31234/osf.io/xvwcy) for more information.\n\nNote: if subsampling (e.g., to balance subreddits), we recommend bootstrapping analyses for unbiased results.\n\n
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.123 | 0.088 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it