Social media and COVID-19 misinformation: how ignorant Facebook users are?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The COVID-19 pandemic has claimed a lot of lives around the world, not only with the virus but also with misinformation. Many researchers have investigated COVID-19 misinformation, but none of them was related to social media users' diverse responses to different types of COVID-19 misinformation, which could be a timely exploration. To bridge this gap in scholarly literature, the present study based on 11,716 comments from 876 Facebook posts on five COVID-19 misinformation seeks to answer two relevant research questions: (a) How ignorant social media users are about misinformation? (b) How do they react to different types of misinformation? Following a quantitative content analysis method, this study produces a few novel findings. The results show that most of the users trust misinformation (60.88%), and fewer can deny (16.15%) or doubt (13.30%) the claims based on proper reasons. The acceptance of religious misinformation (94.72%) surpassed other types of misinformation. Most of the users react happily (34.50%) to misinformation: the users who accept misinformation are mostly happy (55.02%) because it may satisfy their expectations, and the users who distrust misinformation are mostly angry (44.05%) presuming it may cause harm to people. The chi-square and phi coefficient values show strong positive and significant associations between the themes, levels of ignorance, and reactions to misinformation. Some strengths, limitations, and ethical concerns of this study have also been 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.
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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it