Public Discourses About the Opioid Crisis on Social Media
Bibliographic record
Abstract
Abstract When it comes to media discourse surrounding the Canadian opioid crisis, much attention has been paid to news media reporting on opioid harms, interventions, and government initiatives or lack thereof. As social media has been found to be a key source of public health discussions, research has increasingly turned to platforms such as Reddit, Facebook, and Twitter to investigate public discourses relating to opioid usage, harms, and the ongoing crisis. However, there is a distinct lack of empirical studies that examine public discourses on the opioid crisis in general as the majority of previous studies focus on social media surveillance of either specific opioid drugs or opioid drug use. In addition, there remains a clear lack of social media research that focuses on Canadian-specific opioid awareness in comparison to studies that focus on the news media. Hence, this chapter fills a major gap in the literature since it empirically examines mainly Twitter as well as Facebook, Instagram, and Reddit using multimodal analysis that involves both textual and visual assessments. Many social media users blamed persons with opioid addiction and drug gangs as well as politicians and/the government for being behind the opioid crisis. As for their solutions, they mostly discussed the need for effective government action in addition to establishing safe consumption sites and increasing awareness.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".