Who Shares Conspiracy Theories and Other Misinformation about Covid-19 Online: Survey Evidence from Five Countries
Bibliographic record
Abstract
Social media have long been considered a venue in which conspiracy theories and other misinformation incubate and spread. It has been no different during the COVID-19 pandemic. However, understanding who spreads misinformation by sharing it on social media, and why, has been underexplored, especially in a cross-national context. The global nature of the novel coronavirus pandemic presents an opportunity to understand the exposure and sharing of the same COVID-19 misinformation across multiple countries. We rely on nationally representative surveys conducted in July of 2020 and January of 2021 in the United States, United Kingdom, Canada, Australia, and New Zealand, to begin to understand what characterizes those who are most likely to share misinformation online. We find that Americans are no more likely to encounter prominent COVID-19 misinformation online but are considerably more likely to share it. Americans are less likely to say they share misinformation to make others aware of it or to criticize it, and considerably more likely to say their motivation is to promote it or to demonstrate their support for it. Americans are also more likely to say their motivation is to connect with others. In all countries but Canada, those who trust information from social media are more likely to share misinformation than those who do not trust social media. In all countries, those who have populist attitudes and distrust health officials are more likely to share misinformation than those who do not. In the U.S. in particular, sharing misinformation is associated with trust in government and identifying as conservative. Our results make clear that the United States is an outlier. We theorize why this might be the case.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".