#TraumaTok—TikTok Videos Relating to Trauma: Content Analysis
Bibliographic record
Abstract
BACKGROUND: Experiencing a traumatic event can significantly impact mental and emotional well-being. Social media platforms offer spaces for sharing stories, seeking support, and accessing psychoeducation. TikTok (ByteDance), a rapidly growing social media platform, is increasingly used for advice, validation, and information, although the content of this requires further study. Research is particularly needed to better understand TikTok content relating to trauma and the potential implications for young viewers, considering the distressing nature of the subject and the possibility of users experiencing vicarious trauma through exposure to these videos. OBJECTIVE: This study aims to explore the content of trauma-related videos on TikTok, focusing on hashtags related to trauma. Specifically, this study analyzes how TikTok videos present information, advice, stories, and support relating to trauma. METHODS: A quantitative cross-sectional descriptive content analysis was performed on TikTok in December 2022. A total of 5 hashtags related to trauma were selected: #trauma, #traumatized, #traumatok, #traumatic, and #traumabond, with the top 50 videos from each hashtag analyzed (total N=250 videos). A standardized codebook was developed inductively to analyze the content of the videos, while an existing generic codebook was used to collect the video features (eg, age of people in the video) and metadata (likes, comments, and shares) for each video. RESULTS: A total of 2 major content themes were identified, which were instructional videos (54/250, 21.6%) and videos disclosing personal stories (168/250, 67.3%). The videos garnered significant engagement, with a total of 296.6 million likes, 2.3 million comments, and 4.6 million shares, indicating that users find this content engaging and useful. Alarmingly, only 3.7% (9/250) of videos included a trigger warning, despite many featuring highly distressing stories that young people and those with trauma may be exposed to. CONCLUSIONS: The study highlights the potential risks of vicarious trauma due to trauma dumping without trigger warnings on TikTok, and the need for further research to assess the accuracy of advice and information in these videos. However, it also underscores the platform's potential to foster social connections, provide validation, and reduce stigma around mental health issues. Public health professionals should leverage social media to disseminate accurate mental health information, while promoting user education and content moderation to mitigate potential harms. People often use social media, such as TikTok to share advice, stories, and support around mental health, including their experiences with trauma. Out of 250 videos, most were either giving advice (54/250, 21.6%) or sharing personal experiences (168/250, 67.3%). The study found many videos lacked warnings about upsetting content, which could potentially harm young viewers or people suffering from trauma. While TikTok can help people feel connected and reduce the stigma around mental health, it is important to seek support from professionals when needed.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".