The Implied Truth Effect: Attaching Warnings to a Subset of Fake News Headlines Increases Perceived Accuracy of Headlines Without Warnings
Bibliographic record
Abstract
What can be done to combat political misinformation? One prominent intervention involves attaching warnings to headlines of news stories that have been disputed by third-party fact-checkers. Here we demonstrate a hitherto unappreciated potential consequence of such a warning: an implied truth effect, whereby false headlines that fail to get tagged are considered validated and thus are seen as more accurate. With a formal model, we demonstrate that Bayesian belief updating can lead to such an implied truth effect. In Study 1 (n = 5,271 MTurkers), we find that although warnings do lead to a modest reduction in perceived accuracy of false headlines relative to a control condition (particularly for politically concordant headlines), we also observed the hypothesized implied truth effect: the presence of warnings caused untagged headlines to be seen as more accurate than in the control. In Study 2 (n = 1,568 MTurkers), we find the same effects in the context of decisions about which headlines to consider sharing on social media. We also find that attaching verifications to some true headlines—which removes the ambiguity about whether untagged headlines have not been checked or have been verified—eliminates, and in fact slightly reverses, the implied truth effect. Together these results contest theories of motivated reasoning while identifying a potential challenge for the policy of using warning tags to fight misinformation—a challenge that is particularly concerning given that it is much easier to produce misinformation than it is to debunk it. This paper was accepted by Elke Weber, judgment and decision making.
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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 | 0.005 | 0.064 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".