Social media platforms for politics: A comparison of Facebook, Instagram, Twitter, YouTube, Reddit, Snapchat, and WhatsApp
Bibliographic record
Abstract
Citizens have increasingly diversified their use of social media platforms, raising questions about which platforms are adopted and for what purposes. We use survey data from four countries (Canada, France, the United States, and the United Kingdom) gathered in 2019 and 2021 ( n = 12,302) about Facebook, YouTube, Instagram, Twitter, Reddit, Snapchat, and WhatsApp. Political ideology predicts the adoption and political uses of all platforms, but Reddit, Snapchat, and WhatsApp are distinctive. Right-wing users are more likely to report exposure to and posting of political content on these platforms; this pattern is consistent across all four countries. We relate these findings to the distinct network features compared to other platforms. Our large sample size allows us to document a funnel process where large numbers adopt a platform, fewer see political content, and even fewer post. In this funnel process, ideological differences become larger. The findings have implications for the formation of homogeneous communities.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".