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Good to Great Speaker Series

Good to Great Speaker Series

Organized by Assistant Professor Ju Yeon (Julia) Park

April 16, 2024: Michelle Torres, "Beyond Prediction: Identifying Latent Treatments in Images"

Michelle Torres, an assistant professor at UCLA, is a political methodologist with expertise in image analysis using computer vision and machine learning techniques. Her research classifies political visual messages/frames to understand their role in the generation and processing of political information. She will be presenting her recent working paper: "Beyond Prediction: Identifying Latent Treatments in Images." 

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 datasets: 1) a novel experiment measuring attitudes towards climate change in response to visual frames 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 outcomes 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.