Pictures in politics: a visual social semiotics analysis of federal politicians on Instagram
Bibliographic record
Abstract
This major research paper (MRP) examines the visual social semiotics of Canadian politicians’ Instagram accounts and their followers’ responses. As a qualitative study, it seeks to address the following questions: From the coded images in the data collection, which qualities do the Instagram followers prefer? From the coded images in the data collection, which qualities do the Instagram followers prefer the least? What do these qualities reveal about the political actors in the data collection? To answer these questions, I coded the most liked and least liked Instagram postings between April 1, 2014 and March 31, 2015 from two of Canada’s federal party leaders, Prime Minister Stephen Harper and Liberal leader Justin Trudeau. These images were coded through a visual social semiotics analysis under the following categories in my codebook: community outreach, competence, empathy, excitement, ideology, personal, symbols of nationalism, celebrity, and miscellaneous. This codebook was constructed from a literature review encompassing the public’s values of politicians in traditional media. The findings of this MRP expand on visual social semiotic theory in the social media context and build on research about audience perceptions of politicians. The results suggest that photo composition and the presentations of values in an image are important considerations for politicians. Based on the findings, this study is relevant to how professional communicators can construct a persuasive image and story in the political context on a social media platform.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".