Using Twitch for politics? The role of personality across five countries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it