2021/05/19 by Peter Trubowitz, Kohei Watanabe · 33 citations
Social Sciences · #Artificial intelligence #Computer science #Computer security #Contrast (vision) #Elite #Foreign policy #Geopolitics #Index (typography) #International Relations and Foreign Policy #International relations #Law #Newspaper #Political Conflict and Governance #Political science #Politics #Terrorism, Counterterrorism, and Political Violence
paper · pdf · doi:10.1093/isq/sqab029
published in International Studies Quarterly 65(3), 852-865 (Oxford University Press)
openalex publication_date 2021/05/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.