#selfharn on Instagram: understanding online communities surrounding non-suicidal self-injury through conversations and common properties among authors
Why this work is in the frame
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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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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