An Analysis of People’s Emotional Change Toward Vaccines and Its Factors in the Corona Disaster
Bibliographic record
Abstract
The developers of new vaccines against SARS-CoV-2 and governments have provided information on vaccine effectiveness and status on a daily basis to reassure people about vaccination against COVID-19. However, because the interest in vaccines and vaccination status varies by country and region, people do not always feel reassured. In this paper, we analyzed tweets posted on Twitter to elucidate the emotions people have toward COVID-19 vaccines and factors that cause such emotions to be expressed. We selected six countries for our analysis: Japan, the United States, the Great Britain, Canada, Australia, and India, and applied an emotion classification method using machine learning based on the eight types of emotions defined in Plutchik’s wheel of emotions. We also used a text analysis approach using dependency analysis and burst detection methods. The results of our emotion classification showed that fear was the most common emotion in Japan whereas anger and disgust were most common in the United States, Great Britain, Canada, and Australia; joy was most common in India. We also analyzed tweets during the period when a particular emotion was increased in the changes of the emotions represented as a time series based on the burst-detected dependency relations, and found several characteristics: many users posted vaccine-related news, one user would often post a large number of tweets with the same content, and the same event related to vaccines could arouse different emotions depending on the individual’s situation.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".