A Longitudinal Test of Political Self-Effects on Social Media
Bibliographic record
Abstract
This registered report details a longitudinal experiment testing political self-effects – changes in issue-related attitudes and indicators of political self-concept – resulting from political expression on social media. We also tested whether such effects were due to the composition or release of political messages on social media over time. American adults (N = 576), logged on to a fictional social media platform and engaged with discussions involving one of two political issues at three time points over a week. We examined whether participants who wrote a comment about the issue publicly (on the social media platform) or privately (in a text box) experienced changes in their issue-related attitudes or political self-concepts (compared to a control condition). Overall, we did not find consistent main effects of message composition or release at individual timepoints. Instead, message composition sustained issue-related attitudes over time (vs. control). Interaction analyses found that among those with high baseline issue interest, expression (vs. control) led to decreased issue interest and importance over time. Among those high in baseline political interest, message release (vs. control) increased positive issue-related attitudes. We outline an agenda for studying political self-effects that addresses, a) their cumulative nature, b) conditional effects, and c) the challenge of endogeneity.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".