When “Self‐Harm” Means “Suicide”: A Topic Modeling Study of Adolescent Online Help‐Seeking for Self‐Harm
Bibliographic record
Abstract
INTRODUCTION: The 15%-20% of adolescents worldwide who engage in nonsuicidal self-injury (NSSI) face an increased risk of transitioning from suicidal ideation to suicide attempt. To resist NSSI urges, young people often seek peer support online. We examined adolescent help-seeking on a purpose-built online mental health peer support platform, which is a critically understudied help-seeking venue. METHODS: Adolescents' help-seeking posts in the "Self Harm" category on a large online peer support platform (575,261 posts from 114,937 users) were analyzed using topic modeling. We assessed the prevalence of NSSI-related topics versus morbid/suicidal topics. RESULTS: Our 12-topic model produced interpretable themes. Three main findings emerged: posts included little information about the context of self-harm behavior; there was minimal evidence of pro-self-harm content in posts; and the primary topics of the posts were evenly split between NSSI-related topics and morbid/suicidal topics. CONCLUSION: Our findings have important implications for online mental health communities: requiring users to select a narrow category for their post may limit contextual information; moderation of pro-self-harm content may reduce its prevalence; and the absence of dedicated spaces for suicidal users may funnel those users into NSSI-focused spaces, potentially increasing risk for all users.
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.
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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".