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Beyond Prediction: Identifying Latent Treatments in Images

Beyond Prediction: Identifying Latent Treatments in Images

Speaker(opens in a new window)Michelle Torres (Rice University)

Wednesday, October 19, 14:00–14:45 (Irish time)

Please register (opens in a new window)here to receive the link and password to the online meeting and information on the room at UCD.

Abstract: 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.

About the speaker: Michelle Torres is an Assistant Professor at Rice University who specializes in political methodology and political behavior. Her research focuses on making statistical and computer science methods accessible to political scientists. She also develops and applies innovative and rigorous tools to achieve a better understanding of social issues, especially in the fields of political behavior and public opinion. Methodologically, she is interested in computer vision, causal inference and survey methodology. Substantively, she focuses on political communication, participation, and psychological traits.