Are Political Attacks a Laughing Matter? Three Experiments on Political Humor and the Effectiveness of Negative Campaigning
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
Research on the effectiveness of negative campaigning offers mixed results. Negative messages can sometimes work to depress candidate evaluations, but they can also backfire against the attacker. In this article, we examine how humor can help mitigate the unintended effects of negative campaigning using data from three experimental studies in the United States and the Netherlands. Our results show that (1) political attacks combined with “other-deprecatory humor” (i.e., jokes against the opponents) are less likely to backfire against the attacker and can even increase positive evaluations of this latter—especially when the attack is perceived as amusing. At the same time and contrary to what we expected, (2) humor does not blunt the attack: humorous attacks are not less effective against the target than serious attacks. All in all, these results suggest that humor can be a good strategy for political attacks: jokes reduce harmful backlash effects against the attacker, and humoros attacks remain just as effective as humorless ones. When in doubt, be funny. All data and materials are openly available for replication.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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