Online and Unkind: Examining the Personality Correlates of Online Political Incivility
Bibliographic record
Abstract
Many forms of online political incivility threaten democratic norms, contribute to polarization, and are often directed at women and racial minorities. Recent research shows that online political incivility may come from a minority of users that are just as hostile offline as they are online, meaning that individual differences in personality traits may be an important predictor of online political incivility. Drawing upon a large sample of adults living in Canada (N = 1725), we examined the association between personality traits and online political incivility using robust measures of psychopathy, narcissism, Machiavellianism, and the general traits of the HEXACO. While controlling for a variety of sociodemographic and political variables, we found that people who score higher in honesty-humility, agreeableness, and conscientiousness, as well as the planfulness facet of Machiavellianism, are less likely to report engagement in online political incivility. People who score higher in extraversion, several facets of psychopathy, grandiose and vulnerable narcissism, and antagonistic Machiavellianism, by contrast, are more likely to report engagement in online political incivility. In general, the personality traits that predict offline aggression and antisocial behaviour tend to be the same traits that predict self-reports of vulgarity, stereotyping, and threats in online political discussions. Interventions to reduce online incivility may benefit from considering the dispositional tendencies of uncivil users.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".