Michelle Torres Pacheco presents.
TITLE: Understanding visual messages: visual framing and the Bag of Visual Words
Political communication is a central element of several political dynamics. Its visual component is crucial in understanding the origin, characteristics and consequences of the messages sent between political figures, media and citizens. However, visual features have been largely overlooked in Political Science. In this project, I implement computer vision and image retrieval techniques to measure and understand messages conveyed in pictures. More specifically, the article focuses on the analysis of the content and structure of images of Black Lives Matter movement (BLM) protests. For this purpose, the article presents and details the implementation of a Bag of (Visual) Words (BoVW). This method drawn from the field of Computer Science allows researchers to build an Image-Visual Word matrix that emulates the Document-Term matrix in text analysis in order to feed models and classifiers that can provide insights about the content of visual material. Preliminary results from the application of a Structural Topic Model to a corpus of images posted by U.S. newspapers show that conservative outlets tend to include “darker" elements in their depictions of protests: they show more nocturnal events and features like smoke, fire and police patrols than liberal outlets. Overall, the article sheds light on the characteristics and consequences of visual means of communication and persuasion, and provides a useful technique for an accurate analysis and measurement of messages in pictures.