Analyzing tweets before and after Meta’s graphic self-harm imagery ban: a content analysis
Bibliographic record
Abstract
Aim: The spread of suicide and non-suicidal self-injury (NSSI) content on social media has raised ongoing concerns about user safety and mental health. In response, social media platforms like Twitter (now X) and Meta (i.e., Facebook and Instagram) introduced content moderation policies to mitigate harm and promote safer digital environments. This study explored immediate trends in user discourse surrounding suicide and NSSI following the enactment of Meta’s graphic self-harm imagery ban. Specifically, it examined shifts in tweet tone, content type, and underlying themes immediately before and after the policy’s implementation. Methods: A corpus of 3,846 tweets was analyzed. Within this corpus, tweets spanning 32 weeks from October 18, 2018, to May 29, 2019, were selected. These dates were chosen to encompass approximately 16 weeks before and after the enactment of the policy on February 7, 2019. Tweets were categorized according to slant, tweet category, and theme. Results: The findings revealed notable shifts in online discourse. There was a significant decrease in the proportion of tweets identified as anti-self-harm tweets and a corresponding increase in the proportion of tweets aimed at understanding self-harm, many of which were coded as personal opinions or informative content. These trends suggest that while content promoting self-harm did not increase, the tone of discourse shifted toward greater nuance and reflection. This may reflect users’ growing efforts to process, contextualize, and share perspectives on self-harm in a policy-regulated environment. Conclusions: Meta’s graphic self-harm imagery ban appeared to influence how users communicated about suicide and NSSI on Twitter, prompting more content centered on understanding and discussion. However, the findings also highlight challenges in balancing harm reduction with space for personal narratives. These insights emphasize the role of policy in shaping public discourse and the need for clear moderation strategies that distinguish harmful promotion from lived experience and peer support.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".