#selfharn on Instagram: understanding online communities surrounding non-suicidal self-injury through conversations and common properties among authors
Bibliographic record
Abstract
Objectives #selfharm has been blocked by Instagram, but manoeuvring hashtags (e.g. #selfharn) are beginning to appear in order for secret non-suicidal self-injury (NSSI) communities to communicate. The purpose of this study was to (a) determine the nature of the #selfharn conversation on Instagram, (b) analyze common properties of the visual content (i.e. images and videos; n = 93) tagged with #selfharn, and (c) discover what kind of environment the authors ( n = 50) of #selfharn were creating. Methods A multi-method approach was utilized for this study. Netlytic was used to generate a text and content analysis to examine the authors’ captions and comments ( n = 8772) associated with #selfharn (collected over a seven-day period). Results After removing #selfharn from the dataset, the text analysis revealed that #depression ( n = 3081) and #suicide ( n = 2270) were the most commonly used terms associated with #selfharn. Overall, 52% ( n = 4386) of the popular words/phrases related with #selfharn posts were categorized as ‘bad feelings’. Through manual coding, it was determined that the majority of #selfharn visual content ( n = 92; 99%) did not generate an advisory warning but did contain a wound ( n = 70; 75%). The #selfharn author analysis suggests that most were women ( n = 18; 36%) with a dark-coloured profile aesthetic ( n = 37; 74%) determined by an overwhelming amount of grey, black, blue, red, or purple colours. Conclusion According to the text and content analyses, #selfharn on Instagram may be contributing negatively to an online community of mental-health issues. More resources should be provided by Instagram to those who are involved in the NSSI Instagram community.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".