Humor Styles Predict Self-Reported Sarcasm Use in Interpersonal Communication
Bibliographic record
Abstract
We investigated how participants' humor styles impact their sarcasm use. English-speaking participants (N = 179) completed online self-report measures of humor styles and sarcasm use. We conducted linear regressions to test whether their humor style scores could predict their sarcasm use scores. Participants with higher affiliative humor scores reported a greater tendency to use sarcasm in general and to use face-saving sarcasm to protect the social images of the speaker and addressee. People use face-saving sarcasm to enhance their relationships, to tease others, and to self-deprecate. Surprisingly, participants who scored high on aggressive humor reported using face-saving sarcasm often. We suspect this occurred because the aggressive humor and the face-saving scales contain conceptually similar items. Participants with high aggressive humor scores also reported frequently using sarcasm to diffuse frustration. Participants who scored high on self-defeating humor reported often using both face-saving sarcasm and sarcasm to diffuse embarrassment. Given that face-saving sarcasm use was uniquely predicted by affiliative humor, aggressive humor, and self-defeating humor scores, we suggest that face-saving sarcasm use has utility for people with a wide range of humor styles. Our findings highlight how an individual's humor style shapes their flexible use of sarcasm in interpersonal relationships.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".