The Relationship Between Suicide-Related Twitter Events and Suicides in Ontario From 2015 to 2016
Bibliographic record
Abstract
Abstract. Background: Many studies have demonstrated suicide contagion through mainstream journalism; however, few have explored suicide-related social media events and their potential relationship to suicide deaths. Aims: To determine whether Twitter events were associated with changes in subsequent suicides. Methods: Suicide-related Twitter events that garnered at least 100 tweets originating in Ontario, Canada (July 1, 2015 to June 30, 2016) were identified and characterized as putatively "harmful" or "innocuous" based on recommendations for responsible media reporting. The number of suicides in Ontario during the peak of each Twitter event and the subsequent 6 days ("exposure window") was compared with suicides occurring during a pre-event period of the same length ("control window"). Results: There were 17 suicide-related Twitter events during the period of study (12 putatively harmful and five putatively innocuous). The number of tweets per event ranged from 121 for "physician-assisted suicide law in Quebec" to 6,202 for the "Attawapiskat suicide crisis." No significant relationship was detected between Twitter events and actual suicides. Notably, a comprehensive examination of the details of Twitter events showed that even the putatively harmful events lacked many of the characteristics commonly associated with contagion. Limitations: This was an uncontrolled experiment in only one epoch and a single Canadian province. Discussion: This study found no evidence of suicide contagion associated with Twitter events. This finding must be interpreted with caution given the relatively innocuous content of suicide-related Tweets in Ontario during 2015–2016.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".