American Exceptionalism: Determinants of spreading COVID-19 misinformation online in five countries
Bibliographic record
Abstract
Social media have long been considered a venue in which conspiracy theories originate and spread. It has been no different during COVID-19. However, understanding who spreads conspiracy theories by sharing them on social media, and why, has been underexplored, especially in a cross-national context. The global nature of the novel coronavirus pandemic presents a unique opportunity to understand the exposure and sharing of the same COVID-19 related conspiracies across multiple countries. We rely on large, nationally representative surveys conducted in July of 2020 in the United States, United Kingdom, Canada, Australia, and New Zealand, to begin to understand who shares conspiracies on social media and what motivates them. We find that Americans are no more likely to encounter prominent COVID-19 conspiracies on social media but are considerably more likely to subsequently share them. In all countries, trust in information from social media predicts conspiracy theory sharing, while in the US politics plays a unique role Our results make clear that American behavior on social media has the potential to poison online public discourse globally.
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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 | 0.009 |
| 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 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".