Infodemic Exposure and People’s Reliance on COVID-19 Information Sources: Cross-sectional study in ten countries
Bibliographic record
Abstract
This paper explores the relationship between the reliance on different information sources and infodemic exposure in the early phases of the pandemic. The aim is to identify the sources that create less infodemic. We analyze high-quality secondary data from two studies using a 10-country sample: the US, UK, Canada, Brazil, France, Germany, Italy, Japan, South Africa, and South Korea. Study 1 analyzes infodemic exposure through an analysis of 3,723,920 COVID-19 tweets. Likewise, study 2 analyzes reliance on COVID-19 information sources by surveying 10,000 respondents. This research provides perspectives and implications for the infodemic debunking to the government departments, public health organizations, and media industries by analyzing the correlations between these two studies. We found that people who rely on national government information sources about COVID-19 tend to be less exposed to the infodemic. Findings also suggest a correlation between the countries with higher COVID-19 confirmed cases and people’s reliance on the national government information sources. We found that people from countries with more unverified bots tweeting about COVID-19 tend to rely less on family and friends and social media as sources. Evidence also suggests that the most trusted spokespeople are scientists and health professionals rather than politicians. Finally, we observed 70% of the sample´s countries slightly reduced their risk of exposure to the infodemic within 12 months of the pandemic’s start.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".