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.

翻译：本文研究了美国国家政客在推特上分享的视觉媒体，探讨了政客分享的图像类型多样性如何反映其政治立场，并指出了在此背景下使用标准图像表征方法的潜在风险。尽管已有研究在政客使用社交媒体图像方面取得了宝贵成果，但这些研究主要聚焦于摄影类媒体，而此类平台所分享的视觉媒体类型多样（如信息图、插图或表情包），仅关注摄影媒体可能不够全面。本文利用多种流行的预训练深度学习模型来表征政客的视觉内容，通过聚类分析识别出推特上分享的八种视觉媒体类型，其中若干类型并非摄影性质。结果显示，个体政客会分享多种类型的图像，且其图像在不同聚类中的分布与其整体意识形态立场存在相关性——例如，自由派政客倾向于分享更多信息图风格图像，而保守派政客则更常分享爱国主题图像。然而，人工评估发现，这些图像表征模型经常将视觉相似但语义不同的图像归入同一聚类，这影响研究人员对聚类结果及其与政治意识形态相关性的解读。特别是，预训练模型中语义信息的压缩可能导致某些图像聚类出现零结果，而非表明不同意识形态的政客共享相同的图像类型。本文最后提出一系列研究建议，以避免此类问题。