Credibility of vaccine-related content on Twitter during COVID-19 pandemic
Bibliographic record
Abstract
During national COVID-19 vaccine campaigns, people continuously engaged on Twitter to receive updates on the latest public health information, and to discuss and share their experiences. During this time, the spread of misinformation was widespread, which threatened the uptake of vaccines. It is therefore critical to understand the reasons behind vaccine misinformation and strategies to mitigate it. The current research aimed to understand the content of misleading tweets and the characteristics of their corresponding accounts. We performed a machine learning approach to identify misinformation in vaccine-related tweets, and calculated the demographic, engagement metrics and bot-like activities of corresponding accounts. We found critical periods where high amounts of misinformation coincided with important vaccine announcements, such as emergency approvals of vaccines. Moreover, we found Asian countries had a lower percentage of misinformation shared compared to Europe and North America. Our results showed accounts spreading misinformation had an overall 10% greater likelihood of bot activity and 15% more astroturf bot activity than accounts spreading accurate information. Furthermore, we found that accounts spreading misinformation had five times fewer followers and three times fewer verified badges than fact-sharing accounts. The findings of this study may help authorities to develop strategies to fight COVID-19 vaccine misinformation and improve vaccine uptake.
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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 | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".