Infodemic Pathways: Evaluating the Role That Traditional and Social Media Play in Cross-National Information Transfer
Bibliographic record
Abstract
The COVID-19 pandemic has occurred alongside a worldwide infodemic where unprecedented levels of misinformation have contributed to widespread misconceptions about the novel coronavirus. Conspiracy theories, poorly sourced medical advice, and information trivializing the virus have ignored national borders and spread quickly. This information spread has occurred despite generally strong preferences for domestic national media and social media networks that tend to be geographically bounded. How, then, is (mis)information crossing borders so rapidly? Using social media and survey data, we evaluate the extent to which consumption and propagation patterns of domestic and international traditional news and social media can help inform theorizing about cross-national information spread. In a detailed case study of Canada, we employ a large multi-wave survey and a massive data set of Canadian Twitter users. We show that the majority of misinformation circulating on Twitter that is shared by Canadian accounts is retweeted from U.S.-based accounts. Moreover, exposure to U.S.-based media outlets is associated with COVID-19 misperceptions and increased exposure to U.S.-based information on Twitter is associated with an increased likelihood to post misinformation. We thus theorize and empirically identify a key globalizing infodemic pathway: disregard for national origin of social media posting.
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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 | 0.008 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".