Emotional language reduces belief in false claims
Bibliographic record
Abstract
Abstract Emotional appeals are a common manipulation tactic, and it is broadly assumed that emotionality increases belief in misinformation. However, past work often confounds the use of emotional language per se with the type of factual claims that tend to be communicated with emotion. In two experimental studies, we test the effects of manipulating the level of emotional language in false headlines while holding the factual claim constant. We find that, in the absence of a fact-check, the high-emotion version of a given factual claim was believed significantly less than the low-emotion version; in the presence of a fact-check, belief was comparatively low regardless of emotionality. A third experiment found that decreased belief in high-emotionality claims is greater for false claims than true claims, such that emotionality increases truth discernment overall. Finally, we analyze the social media platform X’s Community Notes program, in which users evaluate claims (‘Community Notes’) made by others. We find that Community Notes with more emotional language are less likely to be rated helpful. Our results suggest that, rather than being an effective tool for manipulating people into believing falsehoods, emotional language induces justified skepticism.
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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 | 0.003 | 0.038 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".