Examining Social Support Conversations on Reddit During COVID-19 Using Computational Methods
Bibliographic record
Abstract
Public health crises like the COVID-19 pandemic have posed unprecedented challenges to both physical and mental health. To better understand related social support conversations on online support groups, and how the topics of these conversations are associated with producing conversation and with authors' mental health status, we analyzed 65,004 posts and comments on the subreddit r/COVID19_support using structural topic modeling. Among the 22 valid topics identified, those that attracted more user engagement addressed uncertainty about prospective situations, national and international news, sending condolences regarding loss, and the dangerous impact of the pandemic. More importantly, topics related to giving esteem (e.g. sending encouragement to boost others' self-efficacy, expressing appreciation) and emotional support (e.g. sending regards and condolences) were consistently and negatively associated with authors' anxiety and mental illness during the pandemic. In the same vein, providing informational support by updating situations related to the health impact and political, media, and working environment during the pandemic were also associated with reduced anxiety and mental illness. Theoretical and practical implications are discussed.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".