Exploring Public Reactions to Individuals’ Substance Misuse Recovery Journeys on TikTok
Bibliographic record
Abstract
Background: Social media has become a space for sharing personal experiences and shaping public opinion. This study explored how people respond to substance misuse recovery journeys shared on TikTok. Methods: The researchers collected 3583 comments from 350 TikTok videos under the hashtags #wedorecover, #recovery, and #sobertok using a scraper tool. A discourse analysis categorized comments into Narrative Strategies, Rhetorical Strategies, Linguistic Features, and Power Relationships, each with subcategories revealing public perceptions of substance use and recovery. A correlation analysis was also conducted to examine the role of emojis across narrative and linguistic features. Results: Most comments (94%) expressed support or positivity toward recovery videos. The heart emoji was the most common (93.35% of all emojis), symbolizing connection, encouragement, and solidarity. Four themes emerged reflecting public attitudes: encouragement and positive messaging, acknowledgment of struggle, the culture of sharing, and the influence of broader social narratives. Conclusions: These results provide insight into public responses to recovery content on TikTok, suggesting that peer support may be facilitated through the platform’s algorithmic design. While TikTok shows promise as a supportive digital space, further research is needed to understand its broader implications for substance use recovery support.
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How this classification was reachedexpand
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".