Canadians’ Anti-Masking Attitudes on Twitter During the First Wave of the COVID-19 Pandemic
Bibliographic record
Abstract
Several countries recommended universal masking as a preventive health measure to contain the spread of COVID-19 before public health officials in Canada started endorsing public mask wearing. In the first wave of the COVID-19 pandemic in Canada, public use of face masks was controversial. Many Canadians took to social media (e.g., Twitter) to debate the use of face masks— who should wear which types of masks, when and how often, and why. In this study, we combined computing and social science techniques to extract likely Canadian tweets and qualitatively study Canadians’ anti-masking attitudes during the first wave of the pandemic (January to September 2020). We discuss some beliefs that may have contributed to the emergence of anti-mask sentiment by highlighting five major themes in the Twitter discourse (i.e., face mask efficacy, personal discomfort, perceived risk, rights and freedoms, and culture clash), and also ways that some Canadians attempted to synthesize these contrasting views. Our findings inform public health messaging and strategies for dealing with misinformation during health crises, and point to the role prominent social media figures can play in fostering (or not) a culture of open mindedness.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".