COVMIS: A Dataset for Research on COVID-19 Misinformation
Bibliographic record
Abstract
Combatting misinformation is an important part of the global effort to fight against COVID-19. In this paper, we first present a large-scale, publicly available dataset named COVMIS for research on COVID-19 misinformation. COVMIS was constructed to support the misinformation identification approach that mimics the act of fact checking by human for truth labelling. COVMIS is collected from November 2019 to March 2021, this dataset contains 14, 384 claims (statements), 134, 320 related articles, and many features associated with the claims such as claimants, news sources, dates, truth labels (true, partly true or false) and justifications for the truth labels. Each claim is associated with a set of related articles that were collected from reputable sources and serve as the ground truth to assess the validity of the claim. We provide statistics and a detailed analysis of the dataset, and discuss a variety of its potential use cases. Using COVMIS, we then obtained new experimental results illustrating methods that can be used to significantly improve the performance of the fact checking approach for misinformation identification.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.010 |
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".