Differences in Suicide-Related Twitter Content According to User Influence
Bibliographic record
Abstract
Abstract. Background: The content of suicide-specific social media posts may impact suicide rates, and putatively harmful and/or protective content may vary by the author’s influence. Aims: This study sought to characterize how suicide-related Twitter content differs according to user influence. Method: Suicide-related tweets from July 1, 2015, to June 1, 2016, geolocated to Toronto, Canada, were collected and randomly selected for coding (n = 2,250) across low, medium, or high user influence levels (based on the number of followers, tweets, retweets, and posting frequency). Logistic regression was used to identify differences by user influence for various content variables. Results: Low- and medium-influence users typically tweeted about personal experiences with suicide and associations with mental health and shared morbid humor/flippant tweets. High-influence users tended to tweet about suicide clusters, suicide in youth, older adults, indigenous people, suicide attempts, and specific methods. Tweets across influence levels predominantly focused on suicide deaths, and few described suicidal ideation or included helpful content. Limitations: Social media data were from a single location and epoch. Conclusion: This study demonstrated more problematic content vis-à-vis safe suicide messaging in tweets by high-influence users and a paucity of protective content across all users. These results highlight the need for further research and potential intervention.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".