A thematic analysis of mental health, addiction and gambling discussion on Reddit during the recent cryptocurrency market downturn
Bibliographic record
Abstract
Abstract Purpose Considering the volatility of the cryptocurrency market, it is important to investigate the impact that market participation has on mental health. Therefore, we analyzed Reddit discussions regarding mental health, gambling, and addiction from members of the cryptocurrency discussion board, r/cryptocurrency, during a recent downturn in the cryptocurrency market. Methods We collected 1315 threads submitted to the subreddit r/cryptocurrency between January 3rd to February 4th. A thematic analysis was employed, which included threads that discussed psychological wellbeing, mental health or gambling. Results We thematically analyzed the content of 130 threads, which contained 7635 comments. Our analysis identified three main themes present in user discussion. Theme 1 (emotional state and mental health) captured users' discussion on their wellbeing, mental health and emotional responses to the market downturn. Theme 2 (strategies for coping) examined coping strategies recommended by users to combat distress to the market conditions or trading urges. Theme 3 (likeness to gambling) captured discussion on the relationship between cryptocurrency and gambling based on its fixating properties and risk profile. Conclusions Reddit is a valuable resource for examining the experiences and attitudes of the cryptocurrency community. Accounts of Reddit users' experiences provided insight into the mental distress market downturns can cause and strategies to combat problems due to trading. Our findings offer qualitative insights into the problems experienced by individuals who cryptocurrency trade and encourage further investigation into its relationship with mental health.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".