The Geopolitical Threat Index: A Text-Based Computational Approach to Identifying Foreign Threats

Abstract

Few concepts figure more prominently in the study of international politics than threat. Yet scholars do not agree on how to identify and measure threats or systematically incorporate leaders' perceptions of threat into their models. In this research note, we introduce a text-based strategy and method for identifying and measuring elite assessments of international threat from publicly available sources. Using semi-supervised machine learning models, we show how text sourced from newspaper articles can be parsed to discern arguments that distinguish threatening from non-threatening states, and to measure and track variation in the intensity of foreign threats over time. To demonstrate proof of concept, we use news summaries from The New York Times from 1861 to 2017 to create a geopolitical threat index (GTI) for the United States. We show that the index successfully matches periods in US history that historians identify as high and low threat and correctly identifies countries that have posed a threat to US security at different points in its history. We compare and contrast GTI with traditional indicators of international threat that rely on measures of material capability and interstate behavior.