Examining Similar and Ideologically Correlated Imagery in Online Political Communication

TL;DR

This study analyzes the variety of visual media shared by US politicians on Twitter, revealing correlations with political ideology and highlighting limitations of current image characterization methods.

Contribution

It introduces a multi-model approach to classify diverse visual media types and discusses the implications of semantic collapse in image clustering for political analysis.

Findings

Politicians share diverse types of visual media beyond photographs.

Image sharing patterns correlate with political ideology.

Current models often misclassify images, affecting interpretation.

Abstract

This paper investigates visual media shared by US national politicians on Twitter, how a politician's variety of image types shared reflects their political position, and identifies a hazard in using standard methods for image characterization in this context. While past work has yielded valuable results on politicians' use of imagery in social media, that work has focused primarily on photographic media, which may be insufficient given the variety of visual media shared in such spaces (e.g., infographics, illustrations, or memes). Leveraging multiple popular, pre-trained, deep-learning models to characterize politicians' visuals, this work uses clustering to identify eight types of visual media shared on Twitter, several of which are not photographic in nature. Results show individual politicians share a variety of these types, and the distributions of their imagery across these clusters is correlated with their overall ideological position -- e.g., liberal politicians appear to share a larger proportion of infographic-style images, and conservative politicians appear to share more patriotic imagery. Manual assessment, however, reveals that these image-characterization models often group visually similar images with different semantic meaning into the same clusters, which has implications for how researchers interpret clusters in this space and cluster-based correlations with political ideology. In particular, collapsing semantic meaning in these pre-trained models may drive null findings on certain clusters of images rather than politicians across the ideological spectrum sharing common types of imagery. We end this paper with a set of researcher recommendations to prevent such issues.