Prevalence and effect of misinformation on Twitter during the COVID-19 pandemic: A mixed-methods social media analysis
Bibliographic record
Abstract
Throughout the COVID-19 pandemic, there has been an upward trend of medical misinformation circulating on social media. With the large reach of platforms like Twitter, misinformation can shape the opinions of masses on public health topics and behaviors. This study aims to assess the prevalence of misinformation on Twitter and its impact on the public through a concurrent mixed-methods design. In the quantitative component, we investigated the prevalence of misinformation on Twitter related to COVID-19 transmission, alternative treatments, and vaccines. Twitter shares for the most popular articles were collected at four time periods and misinformation was analyzed for temporal and topical changes. The qualitative component assessed the impact of misinformation by analyzing perspectives towards vaccine acceptance, mask adherence, and lockdown compliance on Twitter. Twitter articles regarding alternative COVID-19 treatments had the most misinformation (47.5%), followed by transmission (20.0%) and vaccines (8.8%). The prevalence of misinformation decreased over time for both alternative treatments and transmission. Conversely, vaccines displayed an increase in misinformation over time. Vaccine acceptance and mask adherence had considerable support; however, some individuals questioned the effectiveness of these measures. Lockdown compliance had mixed support as some supported the enhanced measures while others displayed frustration. Individuals showcased varying opinions on Twitter regarding their willingness to obey public health regulations. Overall, there was a high prevalence of misinformation regarding COVID-19 transmission, alternative treatments, and vaccines during the pandemic.
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 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 | 0.019 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".