Abstract
AbstractFew 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.
Highlights
Few concepts figure more prominently in the study of inter-Peter Trubowitz is a Professor of International Relations and Director of the US Centre at the London School of Economics and Political Science.Kohei Watanabe is a computational social scientist affiliated with Waseda Uninational politics than threat
We show that our model performs well in each of these tests: variations in our geopolitical threat index (GTI) measure correspond to well-known events and periods of relative security and insecurity in US history; the GTI index clearly distinguishes between threatening and non-threatening countries, and recognizes that over time some of America’s friends have become foes, and vice versa; and the GTI index is more granular in how it assesses threats, and responsive to sudden shifts in the international environment, than latent-threat indicators
We conclude by discussing how semi-supervised machine learning models can be used to exploit the full potential of newspaper and other text-based data that international relations scholars rely on to understand political leaders’ foreign policy choices
Summary
Peter Trubowitz is a Professor of International Relations and Director of the US Centre at the London School of Economics and Political Science. Kohei Watanabe is a computational social scientist affiliated with Waseda Uninational politics than threat. Many theories of international politics consider variation in the international threat environment facing countries to be decisive in explaining their versity’s Institute for Advanced Study and the US Centre at the London School of foreign policies and behavior—that is, in explaining lead-.
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