Mining Public Voices: Analyzing Suicide-Related Thoughts and Behaviors in YouTube Videos and Comments Using Topic Modeling
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
YouTube has become a common platform for sharing difficult experiences and sensitive information, including suicide-related thoughts and behaviors. This study analyzes YouTube videos and their comments using topic modeling to explore the common themes discussed within the online community. Our findings show that these videos and comments not only focus on personal stories but also provide encouragement and healthcare-related information, highlighting social media's role in health promotion and peer support. Given that millions of people use various social media platforms to discuss a wide range of topics, these platforms serve as a rich source of data. As such, YouTube videos and comments offer health services researchers a valuable source of public opinion data, providing insights into societal attitudes and perceptions that may differ from those collected through traditional research methods.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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