EXAMINING COVID-19 VACCINE–RELATED AGEISM IN TWITTER DATA
Bibliographic record
Abstract
Abstract During the pandemic, many high-income countries prioritized older adults for COVID-19 vaccination in an attempt to reduce mortality. This prioritization may have exacerbated ageism and intergenerational conflict, especially with the limited quantity of COVID-19 vaccines. This presentation examines vaccine-related ageism during COVID-19 on social media to inform future vaccination campaigns and policies. Using Twitter, we gathered 1,369 relevant tweets using the Twint application in Python from December 8, 2020 to December 31, 2021. Tweets were assessed using inductive thematic analysis and steps were taken to ensure rigor and trustworthiness. Based on our analysis, four main themes were identified including: i) Blame and aggression; ii) Misinformation and mocking content; iii) Ageist political insults; and iv) Challenging ageism. Our study identified issues of false information, hate speech, and ageist political insults that are contributing to intergenerational conflict. Although some tweets challenged this derogatory messaging and demonstrated intergenerational unity, our findings suggest ageism contributed to COVID-19 vaccine hesitancy among older adults. Accordingly, urgent action is required to challenge vaccine misinformation, counter aggressive ageist content, and support intergenerational unity during the pandemic.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| 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".