Are Political Attacks a Laughing Matter? Three Experiments on Political Humor and the Effectiveness of Negative Campaigning
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".