The Role of Personality in Political Talk and Like-Minded Discussion
Bibliographic record
Abstract
Political discussion is a key mechanism for the development of reasoned opinions and political knowledge, but online political discussion has been characterized as uncivil, intolerant, and/or ideologically homogeneous, which is detrimental to this development. In this paper, we examine the role of personality in various forms of political talk—online and offline—as well as like-minded discussion. Based on a 2017 survey conducted in the United Kingdom, United States, and France, we find that people who are open-minded and extraverted are more likely to engage in political talk but less likely to engage in like-minded discussion. Individuals who are older, less educated, introverted, and conscientious are more likely to find themselves in like-minded discussions, both online and on social media. Like-minded discussion is rare; personality, rather than ideology, predicts whether people engage in this form of political talk in online and offline modes. Our findings challenge the role of social media in the creation of like-minded discussion. Instead, we should look to the role of individual attributes, such as personality traits, which create a disposition that motivates the use of social media (and offline networks) to cultivate like-minded discussion.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".