Beyond Prediction: Identifying and Accounting for Latent Treatments in Images

17.04.2023 - 12:00 to 13:30
Location : 
A 5,6 Raum A 231
Type of Event : 
AB B-Kolloquium
Michelle Torres
Lecturer affiliation: 
Rice University, Houston

Images are a rich and crucial element of political communication. The complexity of the information they convey creates challenges for the identification, interpretation, and explanation of the effects of visual messages on information processing and attitude formation. In this article, we adapt a methodological approach used in text analysis, the supervised Indian Buffet Process (sIBP) developed by Fong and Grimmer (F&G, 2016, 2021), to identify latent treatments in images and evaluate their impact on outcomes of interest. First, we use a convolutional neural network (CNN) to decompose images into substantively meaningful and interpretable tokens, visual words, to then form the input of the sIBP. Then, we follow the framework introduced by F&G and demonstrate the utility of this approach using two sets of political images: 1) images from a novel survey measuring perceptions of visual coverage of the migrant caravan from Central America and 2) images of the Black Lives Matter (BLM) movement protests manually labeled by human coders according to the level of conflict they depict. We find significant differences between demographic groups in the way they perceive images, and also unmask latent treatments that confound the relationship between our treatment and outcome of interest. Importantly, this paper extends the usage of computer vision tools in social sciences beyond prediction of image labels to uncovering, understanding, and visualizing the features of images that produce outcomes.