COVID-19 vaccine rumors and conspiracy theories: The need for cognitive inoculation against misinformation to improve vaccine adherence
Bibliographic record
Abstract
INTRODUCTION: Rumors and conspiracy theories, can contribute to vaccine hesitancy. Monitoring online data related to COVID-19 vaccine candidates can track vaccine misinformation in real-time and assist in negating its impact. This study aimed to examine COVID-19 vaccine rumors and conspiracy theories circulating on online platforms, understand their context, and then review interventions to manage this misinformation and increase vaccine acceptance. METHOD: In June 2020, a multi-disciplinary team was formed to review and collect online rumors and conspiracy theories between 31 December 2019-30 November 2020. Sources included Google, Google Fact Check, Facebook, YouTube, Twitter, fact-checking agency websites, and television and newspaper websites. Quantitative data were extracted, entered in an Excel spreadsheet, and analyzed descriptively using the statistical package R version 4.0.3. We conducted a content analysis of the qualitative information from news articles, online reports and blogs and compared with findings from quantitative data. Based on the fact-checking agency ratings, information was categorized as true, false, misleading, or exaggerated. RESULTS: We identified 637 COVID-19 vaccine-related items: 91% were rumors and 9% were conspiracy theories from 52 countries. Of the 578 rumors, 36% were related to vaccine development, availability, and access, 20% related to morbidity and mortality, 8% to safety, efficacy, and acceptance, and the rest were other categories. Of the 637 items, 5% (30/) were true, 83% (528/637) were false, 10% (66/637) were misleading, and 2% (13/637) were exaggerated. CONCLUSIONS: Rumors and conspiracy theories may lead to mistrust contributing to vaccine hesitancy. Tracking COVID-19 vaccine misinformation in real-time and engaging with social media to disseminate correct information could help safeguard the public against misinformation.
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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 | 0.021 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".