Jungseock Joo & Zachary C. Steinert-Threlkeld

Abstract: Images matter because they help individuals evaluate policies, primarily through emotional resonance, and can help researchers from a variety of fields measure otherwise difficult to estimate quantities. The lack of scalable analytic methods, however, has prevented researchers from incorporating large scale image data in studies. This article offers an in-depth overview of automated methods for image analysis and explains their usage and implementation. It elaborates on how these methods and results can be validated and interpreted and discusses ethical concerns. Two examples then highlight approaches to systematically understanding visual presentations of political actors and events from large scale image datasets collected from social media. The first study examines gender and party differences in the self-presentation of the U.S. politicians through their Facebook photographs, using an off-the-shelf computer vision model, Google’s Label Detection API. The second study develops image classifiers based on convolutional neural networks to detect custom labels from images of protesters shared on Twitter to understand how protests are framed on social media. These analyses demonstrate advantages of computer vision and deep learning as a novel analytic tool that can expand the scope and size of traditional visual analysis to thousands of features and millions of images. The paper also provides comprehensive technical details and practices to help guide political communication scholars and practitioners.

For more information, see the article in Computational Communication Research