Helpful or Harmful? An Examination of Viewers' Responses to Nonsuicidal Self-Injury Videos on YouTube
Bibliographic record
Abstract
PURPOSE: To examine viewers' comment responses to nonsuicidal self-injury (NSSI) YouTube videos to determine the potential risks (e.g., NSSI continuation) and benefits (e.g., recovery-oriented social support) of the videos. METHODS: Viewers' comments from the 100 most-viewed NSSI videos on YouTube were examined using two coding rubrics, one for the global nature of comments and one for recovery-oriented themes. Both rubrics were developed using an inductive (bottom-up) approach and had high coding inter-rater reliability (exceeding .80 in all cases). For the global nature of comments, 869 randomly selected comments were evaluated using the rubric, which included 8 coding categories and 22 subcategories. For the examination of recovery-oriented themes, self-disclosure comments (n = 377) were evaluated for nature of recovery statements. RESULTS: Results revealed that the most frequent comments were self-disclosure comments in which individuals shared their own NSSI experiences (38.39%), followed by feedback for the video uploader, including admiration of the video quality (21.95%) or message (17.01%), and admiration for the uploader (15.40%) or encouragement to the video uploader (11.15%). Evaluation of the common self-disclosure comments for recovery-oriented content revealed that the majority did not mention recovery at all (42.89%) and indicated that they were still self-injuring (34.00%). Positive recovery statements were uncommon. CONCLUSIONS: Results suggest that viewers' responses to videos may maintain the behavior (by sharing their own self-injury experiences) and rarely encourage or mention recovery. It is evident that sharing their own experience online is a strong motivator for viewers of NSSI YouTube videos.
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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 | 0.004 | 0.024 |
| 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 | 0.001 |
| 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".