Image as Data: Automated Visual Content Analysis for Political Science
This provides political scientists with tools to analyze visual content data, which offers unique information not available in text, though it is incremental as it builds on existing computer vision methods.
The paper addresses the lack of scalable analytic methods for incorporating large-scale image data in political science by introducing automated computer vision and deep learning techniques, enabling new research questions at scale in areas like political communication and conflict.
Image data provide unique information about political events, actors, and their interactions which are difficult to measure from or not available in text data. This article introduces a new class of automated methods based on computer vision and deep learning which can automatically analyze visual content data. Scholars have already recognized the importance of visual data and a variety of large visual datasets have become available. The lack of scalable analytic methods, however, has prevented from incorporating large scale image data in political analysis. This article aims to offer an in-depth overview of automated methods for visual content analysis and explains their usages and implementations. We further elaborate on how these methods and results can be validated and interpreted. We then discuss how these methods can contribute to the study of political communication, identity and politics, development, and conflict, by enabling a new set of research questions at scale.