“No claims, no trials, no compensation. Just roll up your sleeve”: social media communication on vaccine uptake sentiments in Canada
Bibliographic record
Abstract
Social media platforms are data-rich sources of communication. We analysed 20,000 Canadian COVID vaccine tweets at two Canadian geographic and temporal levels of before and after vaccine arrivals in two provinces of British Columbia (BC) with the highest COVID vaccine uptake and Ontario (ON) with the lowest uptake. Using machine learning based sentiment analysis and topic modeling and drawing on framing theory, the positive and negative emotional sentiment were framed into actionable subframes to inform future vaccine propagation efforts. The vaccine negative sentiments that emerged were framed from a constellation of insecurities and categorized into four intertwined frames and themes: health information mavenism framed misinformation and conspiracy beliefs (20% and 16% post-vaccine reduction in BC and ON); constellation of insecurities framed antivaccine expressions and hesitancy (9% and 6% drop in BC and ON post- vaccine); pandemic of mistrust and opinions framed lack of faith and skepticism (post-vaccine arrival increased by 19% in BC and dropped by 6% in ON) and opinions and perceptions framed fear, death and worry (stable over time in BC and 20% increased in ON). Vaccine propagation efforts should consider over time disappearing misinformation and conspiracies while attentive to lingering conspiracies creating fears and worries of death.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".