An investigation of the COVID-19-related fake news sharing on Facebook using a mixed methods approach
Bibliographic record
Abstract
This study investigates the factors associated with sharing fake news about COVID-19 on Facebook. The authors developed a model comprising novel constructs to analyze the motivations for sharing COVID-19-related fake news on Facebook based on the theoretical framework of rumor theory and the boomerang effect. The study employed a mixed-methods approach, including an online survey with 338 respondents, which was analyzed using a complementary exploratory design strategy. Additionally, the authors developed a fuzzy-set qualitative comparative analysis (fsQCA) and compared different approaches for Structural Equation Modeling (SEM) in R language and SmartPLS software. The study revealed that respondents with higher levels of education reported higher literacy skills and that users with higher literacy skills were less likely to trust and share fake news content. Furthermore, the trust predicted fake news sharing. The study also identified intrinsic and extrinsic motivators for sharing fake news. The findings underscore the complex nature of fake news sharing and the need for a nuanced approach when addressing the issue. In practical terms , the study suggests combating fake news by training users to improve their literacy skills and addressing the culture of information sharing and user responsibility over the information shared .
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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 | 0.021 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".