Visual Self-Presentation Strategies of Political Candidates on Social Media Platforms: A Comparative Study
Bibliographic record
Abstract
This study investigates the visual self-presentation of political candidates on different social media platforms (Facebook, Instagram, and Twitter) in seven Western democracies (Austria, Canada, France, Germany, Norway, the United Kingdom, and the United States). Drawing on Grabe and Bucy’s visual framing approach, I conducted a quantitative content analysis of visual social media posts (N = 2,272) of the top two candidates who ran for the chief executive governmental office in the respective election campaigns. The results reveal that candidates are more likely to use the ideal candidate frame than that of the populist campaigner. The use of visual frames differs significantly among countries, but those differences are limited. It seems that differences among candidates within countries are more pronounced than among countries. The results also indicate that Instagram is the preferred platform for visual self-presentation. This study provides insights into the strategic use of visuals in social media campaigning.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".