Using Twitch for politics? The role of personality across five countries
Bibliographic record
Abstract
Twitch is a popular live-streaming platform primarily used in the context of gaming. Streamers tend to be very sensitive to the content shared in their streams, often eschewing political content. At the same time, the platform is increasingly used by political actors, activists, journalists and political influencers. In this study, we offer insights into the extent to which Twitch is used for political purposes using a five-country (US, UK, France, Canada, and Germany) survey collected in 2023 (n = 7,500). With this large sample, we are able to focus on a subset of respondents who use Twitch (n = 1,552). We examine the role of the Big Five personality traits in explaining exposure to political information and posting of political content on Twitch. Extraversion positively relates to political information and posting on Twitch, whereas agreeableness and conscientiousness negatively relate to both. This study is important because citizens are diversifying their platform use and little is known about Twitch and its political uses. Already, Twitch content reaches a significant group, particularly those who are young, male, politically interested, and identify as right-wing. Understanding this user group helps explain political behaviors on a widely used but understudied platform.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".