The Scope of Nonsuicidal Self-Injury on YouTube
Bibliographic record
Abstract
OBJECTIVE: Nonsuicidal self-injury, the deliberate destruction of one's body tissue (eg, self-cutting, burning) without suicidal intent, has consistent rates ranging from 14% to 24% among youth and young adults. With more youth using video-sharing Web sites (eg, YouTube), this study examined the accessibility and scope of nonsuicidal self-injury videos online. METHODS: Using YouTube's search engine (and the following key words: "self-injury" and "self-harm"), the 50 most viewed character (ie, with a live individual) and noncharacter videos (100 total) were selected and examined across key quantitative and qualitative variables. RESULTS: The top 100 videos analyzed were viewed over 2 million times, and most (80%) were accessible to a general audience. Viewers rated the videos positively (M = 4.61; SD: 0.61 out of 5.0) and selected videos as a favorite over 12 000 times. The videos' tones were largely factual or educational (53%) or melancholic (51%). Explicit imagery of self-injury was common. Specifically, 90% of noncharacter videos had nonsuicidal self-injury photographs, whereas 28% of character videos had in-action nonsuicidal self-injury. For both, cutting was the most common method. Many videos (58%) do not warn about this content. CONCLUSIONS: The nature of nonsuicidal self-injury videos on YouTube may foster normalization of nonsuicidal self-injury and may reinforce the behavior through regular viewing of nonsuicidal self-injury-themed videos. Graphic videos showing nonsuicidal self-injury are frequently accessed and received positively by viewers. These videos largely provide nonsuicidal self-injury information and/or express a hopeless or melancholic message. Professionals working with youth and young adults who enact nonsuicidal self-injury need to be aware of the scope and nature of nonsuicidal self-injury on YouTube.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".