Natural language processing reveals vulnerable mental health support groups and heightened health anxiety on Reddit during COVID-19.
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".