Can We Algorithmize Politics? The Promise and Perils of Computerized Text Analysis in Political Research

Abstract

In recent years, political scientists increasingly have used data-science tools to research political processes, positions, and behaviors. Because both domestic and international politics are grounded in oral and written texts, computerized text analysis (CTA)—typically based on natural-language processing—has become one of the most notable applications of data-science tools in political research. This article explores the promises and perils of using CTA methods in political research and, specifically, the study of international relations. We highlight fundamental analytical and methodological gaps that hinder application and review processes. Whereas we acknowledge the significant contribution of CTA to political research, we identify a dual “engagement deficit” that may distance those without prior background in data science: (1) the tendency to prioritize methodological innovation over analytical and theoretical insights; and (2) the scholarly and political costs of requiring high proficiency levels and training to comprehend, assess, and use advanced research models.

Using OpenAI models as a new tool for text analysis in political leaders’ unstructured discourse

“…However, it is essential to note that using artificial intelligence also raises ethical and methodological concerns. The reliance on these automated processes can introduce certain biases and inaccuracies in data analysis (Mitrani et al, 2022). Furthermore, using AI raises questions about manipulation and the potential for spread ing misinformation (Fagni et al, 2021).…”