Image as Data: Automated Visual Content Analysis for Political Science

TL;DR

This paper introduces automated computer vision and deep learning methods for analyzing large-scale visual content data, enabling political science research to incorporate images for studying political events, actors, and interactions.

Contribution

It provides an overview of scalable automated visual content analysis methods and discusses their applications and validation in political science research.

Findings

Automated methods enable analysis of large visual datasets in political science.

Visual data can reveal insights not accessible through text data.

The paper discusses validation and interpretation of visual analysis results.

Abstract

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.