Catching Zika Fever: Application of Crowdsourcing and Machine Learning\n for Tracking Health Misinformation on Twitter
Bibliographic record
Abstract
In February 2016, World Health Organization declared the Zika outbreak a\nPublic Health Emergency of International Concern. With developing evidence it\ncan cause birth defects, and the Summer Olympics coming up in the worst\naffected country, Brazil, the virus caught fire on social media. In this work,\nuse Zika as a case study in building a tool for tracking the misinformation\naround health concerns on Twitter. We collect more than 13 million tweets --\nspanning the initial reports in February 2016 and the Summer Olympics --\nregarding the Zika outbreak and track rumors outlined by the World Health\nOrganization and Snopes fact checking website. The tool pipeline, which\nincorporates health professionals, crowdsourcing, and machine learning, allows\nus to capture health-related rumors around the world, as well as clarification\ncampaigns by reputable health organizations. In the case of Zika, we discover\nan extremely bursty behavior of rumor-related topics, and show that, once the\nquestionable topic is detected, it is possible to identify rumor-bearing tweets\nusing automated techniques. Thus, we illustrate insights the proposed tools\nprovide into potentially harmful information on social media, allowing public\nhealth researchers and practitioners to respond with a targeted and timely\naction.\n
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".