The Role of Political Interest in the Relationships Between Privacy Concerns, Social Network Size, and Political Expression on Facebook, Twitter, and Instagram
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
Studies on the predictors of using social media for political purposes reveal some unexpected complexities: users often disregard institutional privacy concerns to discuss politics online, and the size of social networks positively correlates with political expression on social media. Building on the privacy calculus theory, we explore how political interest interacts with privacy concerns and social network size when users decide to engage in political expression on social media. This study utilizes survey data from four countries (the US, UK, France, and Canada) collected in 2019 ( n = 6,291), encompassing three social media platforms: Facebook, Instagram, and Twitter. We find that privacy concerns are negatively related to expression on social media. Larger social networks positively relate to political expression, especially on Twitter. Political interest plays an important moderating role: highly politically interested users discount privacy concerns and opt to post political content. These findings replicate across all three platforms.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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