‘Vaccinfluencers’: a study of influential voices criticizing COVID-19 vaccination efforts and negative vaccine information discourse on Twitter
Bibliographic record
Abstract
In late 2020, the large-scale rollout of COVID-19 vaccines to combat the global pandemic ignited a firestorm of debates and media discourse on vaccines. We conducted a discourse analysis of tweets (n = 875) criticizing the COVID-19 vaccination process and/or containing negative vaccine information (NVI) authored by influential Twitter accounts receiving the highest user engagement. Results showed news media and private citizens to be important influencers of NVI discourse criticizing the COVID-19 vaccination process on Twitter. The most frequently expressed beliefs centered around ineffective vaccine policies and inadequate government responses. A content analysis revealed that on average, tweets criticizing a broader inadequate public health response were the most retweeted. Statistically significant differences in vaccine discourse were found between Canada and the United States, underscoring the importance of local context-specific factors that influence how Twitter users construct issues related to COVID-19 vaccination. Our results suggest that satisfaction with the leaders in charge of the rollout of COVID-19 vaccines may have depended more on how those leaders acted rather than actual vaccination rates. Studying concerns and criticisms toward vaccination and NVI are key to identifying areas of change in vaccine policies and programs that citizens and other actors want to see implemented.
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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 | 0.018 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".