Social media, visuals, and politics: a look at politicians' digital visual habitus on Instagram
Bibliographic record
Abstract
Chapter 13: While visuals have been an important component of mass mediated political communication over the last five decades, they have been thrust to the forefront of politics in recent years with the development and popularization of visual-centric and largely identity-driven social media services (e.g. Instagram, SnapChat, TikTok). Visuals have also grown to become a more prominent feature of the user-generated content shared on text-based social media platforms, including Facebook and Twitter. As these channels are playing an increasingly central role in the political media diet of members of the public, established political elites - including elected officials and candidates running for office - have been turning more frequently to these tools when conducting their day-to-day public political outreach and engagement operations. This book chapter zeroes in on politicians' uses of visuals to appeal to and connect with specific segments of the audience. Building on French sociologist Pierre Bourdieu's scholarship and more recent work of social scientists who have studied contemporary visual political communication, this chapter puts forth the "digital visual habitus" model. This model breaks down and characterizes the ways in which visuals are used by politicians for political image-making, namely by highlighting aspects of their identity, personalizing their public political image, and making themselves - and their political and policy viewpoints - relatable and appealing to members of the public. In other words, it drills down on how politicians are turning to still and moving-image content to foster greater levels of perceived political authenticity among the public. This "digital visual habitus" model is twofold. On the one hand, it considers how internal factors (e.g. political affiliations, preferences, family life) are shaping politicians' uses of visual cues when crafting their public image on social media. On the other hand, it unpacks the effects of the external environment (e.g. social media processes, audience expectations, political environment) on how they are rolling out and adapting their public image to their ever-evolving social and political context. In order to do so, this chapter unpacks politicians' presence on and uses of Instagram during the 2016 and 2020 U.S. presidential elections as well as the 2019 Canadian federal elections. As the study of visual political communication in the social mediascape is an interdisciplinary field of academic inquiry that has grown rapidly over the last years, this chapter contributes a model that will fuel future research.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".