Quality and Reliability of Adolescent Sexuality Education on Chinese Video Platforms: Sentiment-Topic Analysis and Cross-Sectional Study
Bibliographic record
Abstract
Background Adolescence is a critical period for lifelong health, which makes access to accurate and comprehensive sexuality education essential. As video platforms become a primary source of information for adolescents, the quality of their content significantly impacts their physical and mental health. Objective This study aimed to evaluate the quality, reliability, understandability, and actionability of adolescent sexuality education videos on major Chinese platforms (Bilibili, TikTok or Douyin, and Kwai), analyze associated user comment sentiment and topics, identify predictors of quality and reliability, and provide recommendations. Methods A cross-sectional analysis was conducted (April 2025) on the top 100 comprehensively ranked comprehensive sexuality education videos (N=300 total) retrieved from each platform using the keyword 青春期性教育 (“adolescent sexuality education”). Videos were assessed using the Global Quality Score, modified DISCERN, and Patient Education Materials Assessment Tool (PEMAT-U/A), with interrater reliability assessed via Cohen κ. A corpus of over 49,000 user comments underwent sentiment analysis (fine-tuned RoBERTa) and topic modeling (BERTopic, yielding 29 topics grouped into 6 themes). Statistical analyses included Kruskal-Wallis H tests, Spearman correlations, and stepwise linear regressions (SPSS [version 27.0]; P<.05). Results Video quality and reliability were moderate on Bilibili and TikTok but generally poor on Kwai. Content from verified sources (physicians, educators, and institutional media) demonstrated superior quality and stability compared to highly variable content from individual media (the predominant source type, especially on Kwai; 87/100, 87%). Paradoxically, Kwai exhibited the highest user engagement despite the lowest quality scores. Understandability (PEMAT-U) was consistently the strongest positive predictor for both quality (Global Quality Score, final model adjusted R2=0.383, β=0.485) and reliability (modified DISCERN, final model adjusted R2=0.209, β=0.319). Actionability (PEMAT-A) and video duration were also significant positive predictors. Understandability scores (PEMAT-U) were generally high (approximately 69%), while actionability scores (PEMAT-A) were moderate to low (33%-50%). Sentiment analysis revealed that comments were predominantly neutral (35,372/49,680, 71.2%), with negative comments (9141/49,680, 18.4%) significantly outweighing positive ones (5167/49,680, 10.4%). Key discussion themes identified included sources of knowledge acquisition, sexual safety and prevention, physiology, and sexual health and practices. Conclusions While online video platforms offer accessible channels for adolescent sexuality education in China, the current content is often of moderate-to-poor quality, with questionable reliability and limited actionability. Understandability is paramount, but high engagement does not necessarily correlate with high quality or reliability, potentially amplifying misinformation. To effectively empower youth, critical steps include enhancing content quality by adhering to evidence-based frameworks like the International Technical Guidance on Sexuality Education; strengthening platform accountability through improved verification and algorithms; and promoting user media literacy. These measures aim to foster a healthier and more equitable future for Chinese adolescents, helping to achieve goals related to sexually transmitted infections and pregnancy prevention and promoting more open societal attitudes toward sexuality.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".