Understanding the landscape and propagation of COVID-19 misinformation and its correction on Sina Weibo
Bibliographic record
Abstract
The prevalence of health misinformation on social media could significantly influence individuals’ health behaviors. To examine the prevalent topics, propagation, and correction of coronavirus disease 2019 (COVID-19) misinformation, automated content analyses were conducted for posts on Sina Weibo, which is China’s largest microblogging site. In total, 177,816 posts related to COVID-19 misinformation during the COVID-19 outbreak in China were analyzed. The structural topic modeling identified 23 valid topics regarding COVID-19 misinformation and its correction, which were further categorized into three general themes. Sentiment analysis was conducted to generate positive and negative sentiment scores for each post. The zero-inflated Poisson model indicated that only the negative sentiment was a significant predictor of the number of comments (β = 0.003, p < 0.001) but not reposts. Furthermore, users are more prone to repost and comment on information regarding prevention/treatment (e.g., traditional Chinese medicine preventing COVID) as well as potential threats of COVID-19 (e.g., COVID-19 was defined as an epidemic by World Health Organization). Health education and promotion implications are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".