  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.
