Social media, visuals, and politics: a look at politicians' digital visual habitus on Instagram
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,004 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».