The Hydroxychloroquine Twitter War: A case study examining polarization in science communication
Bibliographic record
Abstract
The COVID-19 pandemic has created communication challenges exacerbated by the circulation of misinformation and the politicization of science. The case of hydroxychloroquine is an illustrative example, with the drug being aggressively promoted as a cure even while emerging evidence demonstrated the contrary. This research analyzed how hydroxychloroquine discussions took place on Twitter from 21 to 28 April 2020, a key period in developments around the drug. We collected, in real time, tweets with “hydroxychloroquine” over this period, which resulted in a dataset of nearly one million tweets from over 350,000 Twitter accounts. Our content analysis provides specific details of how hydroxychloroquine was promoted and critiqued, and which accounts were tweeting. Findings showed a highly polarized environment with active bots and conspiracy propagators, where political perspectives dominated the Twittersphere in the place of science-focused discussions.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".