Do you have depression? A summative content analysis of mental health-related content on TikTok
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background TikTok is a global social media platform with over 1 billion active users. Presently, there are few data on how TikTok users navigate the platform for mental health purposes and the content they view. Objective This study aims to understand the patterns of mental health-related content on TikTok and assesses the accuracy and quality of the advice and information provided. Methods We performed a summative content analysis on the top 1000 TikTok videos with the hashtag #mentalhealth between October 12 and 16, 2021. Six content themes were developed to code the data: (1) a personal story, perspective, or confessional, (2) advice and information, (3) emoting, (4) references to death, (5) references to science or research, and (6) a product or service for sale. Advice and information were further assessed by clinical experts. Results A total of 970 mental health-related videos were pulled for our analysis ( n = 30 removed due to non-English content). The most prevalent content themes included a personal story, perspective, or confessional ( n = 574), advice and information ( n = 319), emoting ( n = 198), references to death ( n = 128). Advice and information were considered misleading in 33.0% of videos ( n = 106), with misleading content performing better. Few videos included references to scientific evidence or research ( n = 37). Conclusion Healthcare practitioners and researchers may consider increasing their presence on the platform to promote the dissemination of evidence-based information to a wider and more youth-targeted population. Interventions to reduce the amount of misinformation on the platform and increase people's ability to discern between anecdotal and evidence-based information are also warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it