Who Fact-Checks and Does It Matter? Examining the Antecedents and Consequences of Audience Fact-Checking Behaviour in Hong Kong
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
This study investigated the ways in which people engaged in fact-checking in a highly divided context—the Anti-Extradition Bill Movement (AEBM) in Hong Kong. A telephone survey representative of the Hong Kong population was conducted in 2020 ( N = 1,004). The findings showed that males with greater news consumption and issue involvement were more likely to engage in fact-checking behavior. Nevertheless, the effects of fact-checking appeared mixed. We first found that fact-checking behavior reduced belief in disagreeable misinformation only for supporters of the AEBM. More robust evidence showed that frequent fact-checking behavior reinforced, rather than reduced, partisans’ belief in misinformation regarding the opponent group. A warning of the backfire effects of fact-checking on exacerbating opinion polarization and social division is issued.
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 | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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