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 <i>decreases</i> 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 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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.139 | 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 teacher head, 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".