“The mask is not for you” : A framing analysis of pro- and anti-mask sentiment on Twitter
Bibliographic record
Abstract
In the midst of the COVID-19 pandemic, widespread adoption of facemasks has been recognized as a low-cost, simple public health intervention that can reduce the transmission of the virus. However, early in the pandemic significant public opposition emerged in the U.S. and other parts of the world. So-called “anti-maskers” argue that COVID-19 is a hoax or the threat is overblown, that facemasks are ineffective, or that mask mandates infringe on personal rights and freedoms. Social media platforms can play an important role in shaping public sentiment about health issues, as well as circulating harmful misinformation. Researchers can also study social media data to better understand public perceptions and dominant discourses. This study examines four prominent mask-related hashtags on Twitter across three different time periods early in the pandemic. A content analysis of these tweets was used to investigate pro- and anti-mask wearing sentiment, the motivations behind these beliefs, the rhetorical strategies and themes present in these communications, and changes over time. Of the 600 tweets collected, 440 were pro-mask wearing, 134 were anti-mask wearing, and 26 did not declare a position. Pro-mask tweets used evidence at a rate of 28%, while anti-mask tweets used evidence at a rate of 16%. The most common motivation for a pro-mask position was mask-wearing as a civic duty, and the most common motivation for an anti-mask position was standing up to government tyranny. There were 68 tweets that expressed distrust in institutions, 97% of which were anti-mask, 44% mentioned a conspiracy theory while only 18% used evidence to support their position. It was found that mask sentiment on Twitter encompasses a variety of themes, worldviews, and rationales. Public health messaging must go beyond information transmission, and account for the complexity of the social, political, and economic factors which influence belief and behavior.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".