Self-Harm and Suicide-Related Content on TikTok: Thematic Analysis
Bibliographic record
Abstract
BACKGROUND: Social media platforms, such as TikTok, may be powerful vectors for transmission of both harmful and helpful self-harm and suicide-related content; however, this has not been rigorously studied. OBJECTIVE: This study aims to identify and understand the themes and overall characteristics of videos related to self-harm and suicide on TikTok. METHODS: Snowball sampling was used to identify the 10 most-viewed TikTok hashtags related to self-harm and suicide, which were then used to select the most-viewed English-language posts up to June 2023. An inductive coding reliability approach to thematic analysis was iteratively applied by 2 independent coders to identify and analyze common themes within the videos. RESULTS: In total, 188 videos were included in the thematic analysis. Five main themes and 2 subthemes were identified: emotional distress, hope and recovery-based messaging, grief and memorialization of those who died by suicide, social functions associated with self-harm and suicide-related content (subthemes: gallows humor and sarcasm; glamorization of self-harm and suicide-related behavior), and shame and guilt associated with self-harm and suicide-related behavior. CONCLUSIONS: Self-harm and suicide-related content on TikTok was diverse, encompassing both potentially harmful (eg, normalization of self-harm behavior) and helpful (eg, recovery-focused messaging) characteristics. Therefore, a multifaceted and collaborative approach is needed to address the risks of potentially harmful content while leveraging the positive characteristics to promote the safety and well-being of TikTok users.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".