Visual Polarization Measurement Using Counterfactual Image Generation
This addresses the problem of understanding non-verbal political polarization in media for researchers and policymakers, though it is incremental as it extends existing polarization studies to visual content using new methods.
The paper tackled the problem of measuring political polarization in visual content from news media, which has been understudied due to image complexity, by introducing the PMCIG method that combines economic theory with generative models and multi-modal deep learning. The result, applied to a decade-long dataset of 30 politicians across 20 outlets, identified significant polarization with variations, such as Fox News favoring Republicans and The New York Times favoring Democrats, and Donald Trump and Barack Obama being among the most polarizing figures.
Political polarization is a significant issue in American politics, influencing public discourse, policy, and consumer behavior. While studies on polarization in news media have extensively focused on verbal content, non-verbal elements, particularly visual content, have received less attention due to the complexity and high dimensionality of image data. Traditional descriptive approaches often rely on feature extraction from images, leading to biased polarization estimates due to information loss. In this paper, we introduce the Polarization Measurement using Counterfactual Image Generation (PMCIG) method, which combines economic theory with generative models and multi-modal deep learning to fully utilize the richness of image data and provide a theoretically grounded measure of polarization in visual content. Applying this framework to a decade-long dataset featuring 30 prominent politicians across 20 major news outlets, we identify significant polarization in visual content, with notable variations across outlets and politicians. At the news outlet level, we observe significant heterogeneity in visual slant. Outlets such as Daily Mail, Fox News, and Newsmax tend to favor Republican politicians in their visual content, while The Washington Post, USA Today, and The New York Times exhibit a slant in favor of Democratic politicians. At the politician level, our results reveal substantial variation in polarized coverage, with Donald Trump and Barack Obama among the most polarizing figures, while Joe Manchin and Susan Collins are among the least. Finally, we conduct a series of validation tests demonstrating the consistency of our proposed measures with external measures of media slant that rely on non-image-based sources.