Does Third-Party Fact-Checking Increase Trust in News Stories? An Australian Case Study Using the “Sports Rorts” Affair
Bibliographic record
Abstract
Given the centrality of news media to democracy, it is concerning that public trust in media has declined in many countries. A potential mechanism that may reverse this trend is independent fact-checking to adjudicate competing claims in news stories. We undertake a survey experiment on a sample of 1608 Australians to test the effects of fact-checking on media trust using a real-life case study known as the “sports rorts” affair. We construct duplicate news articles from two national media outlets (i.e. ABC.net.au, news.com.au) containing a senior government minister’s real-life false claim that public funds were not used for political advantage immediately before an election. Half of the participants are exposed to a third-party fact check, which confirms the Minister’s claim is verifiably false, the other half are not. All respondents are asked to evaluate the story’s and news outlets’ trustworthiness. Contrary to our expectations we find a backfire effect whereby independent fact-checking decreases readers’ trust in the original news story and outlet. This negative relationship is not conditional on partisanship or the media source. Our study provides a cautionary tale for those expecting third-party fact-checks to increase media trust and we outline several avenues by which fact-checkers might overcome this.
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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 | 0.017 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".