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 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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".