The COVID-19 Infodemic: Misinformation About Health on Social Media in Istanbul
Bibliographic record
Abstract
Misinformation and conspiracy theories can spread as quickly as the COVID-19 pathogen itself. The infodemic, which describes false or misleading information about this recent epidemic on the internet, has become a serious problem all over the world, and has been declared as an “enemy” by the World Health Organization. In this sense, in order to combat the epidemic, it becomes important to reveal the nuances of COVID-19 related infodemic available on the internet. Particularly, internet users in Turkey are increasingly utilizing social media –a platform synonymous with misinformation– to access news coverage regarding the pandemic (World Health Organization, 2020). In this quantitative study focusing on the city of Istanbul (n=399), which is at the epicenter of the outbreak in Turkey, the social media usage of individuals, their trust in these platforms, exposure to misinformation and conspiracy theories, and fact-checking behaviors were examined. Our results indicate that participants tended to believe in misinformation and conspiracy theories rather than confirming information through fact-checking platforms. Nearly half of all participants believed at least one of four widespread conspiracy theories about the virus. Moreover, when fact-checking did identify misinformation, the participants’ trust in social media showed a slight decrease. Based on these findings, our study proposes a comprehensive model for pandemic-related trust, misinformation, conspiracy theories, and fact-checking factors on digital platforms.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".