An analysis of the COVID-19 Infodemic: The impact of American public sources on sentiment, conversation, and physician behaviour towards hydroxychloroquine
Bibliographic record
Abstract
The COVID-19 infodemic, described as an overabundance of both accurate and inaccurate information, poses a significant public health risk in spreading fear and provoking inappropriate prescription. The overwhelming and often contradictory information on as potential treatments for COVID-19 have contributed to this infodemic. Public sources including the US federal government, health organizations, and research publications have released conflicting statements on the efficacy of hydroxychloroquine. Previous research has not analyzed the influence of these sources on public attitudes and conversation towards the drug. To evaluate this impact, changes in the number and sentiment of tweets tagged with the hashtag or keyword “hydroxychloroquine” from March 12th to June 22nd, 2020 in relation to public sources were analyzed. We found that the US government had a statistically significant influence on public attitudes and behaviour (p < 0.001), unlike health organizations and research publications. Public sentiment on hydroxychloroquine has also been observed to become more negative over time, suggesting that public attitudes towards controversial topics can change. This study also found a positive correlation between public sentiment of hydroxychloroquine and other drugs (i.e. azithromycin and remdesivir) which indicates that public sources disseminating hydroxychloroquine-related information could also affect public attitudes towards related treatments. In a public health crisis, all statements and actions from public sources regarding contentious topics like hydroxychloroquine should be made with caution. To mitigate the disproportionate influence of public sources in an infodemic, we recommend three solutions: (a) education to empower individuals of all ages to develop critical thinking and digital literacy skills; (b) stronger action from social media platforms in labeling misinformation; (c) and cooperation between entities with strong influence (e.g. federal government) and other sources for public health measures. Together, these recommendations could resolve shortcomings existent with a single approach. Future research should be conducted with a custom trained model for sentiment analysis. It would also be valuable to conduct a similar version of the study on other social media platforms as well as for public health issues beyond COVID-19.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".