The Nature of Online Talk: Incivility of Opposing Views and Affective Polarization
Bibliographic record
Abstract
Affective polarization is on the rise. Increasing polarization is often attributed to the nature of political discussion on social media platforms, but little is known about the affective consequences of the incivility of online discussion. This study adopts a trust game to consider whether people punish the incivility of both out-partisans and co-partisans and whether there are gender-related differences in punishment. It also examines whether incivility can have spillover effects on broader out-party hostility. Five pre-registered hypotheses are tested using a pair of survey experiments fielded to a sample (N=974) of adult Canadian partisans. We find that participants punish the incivility of co-partisans but not out-partisans. However, incivility may spill over and heighten hostility towards out-parties more generally. Finally, we do not find evidence that women are more likely to be punished for incivility. Our findings highlight significant nuance with respect to the effects of incivility on trust and affective polarization, as well as the expectations of civility people hold for individuals online.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".