An Image is Worth $K$ Topics: A Visual Structural Topic Model with Pretrained Image Embeddings
Abstract
Political scientists are increasingly interested in analyzing visual content at scale. However, the existing computational toolbox is still in need of methods and models attuned to the specific challenges and goals of social and political inquiry. In this article, we introduce a visual Structural Topic Model (vSTM) that combines pretrained image embeddings with a structural topic model. This has important advantages compared to existing approaches. First, pretrained embeddings allow the model to capture the semantic complexity of images relevant to political contexts. Second, the structural topic model provides the ability to analyze how topics and covariates are related, while maintaining a nuanced representation of images as a mixture of multiple topics. In our empirical application, we show that the vSTM is able to identify topics that are interpretable, coherent, and substantively relevant to the study of online political communication.