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

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.

在国际政治学研究领域，鲜有概念能比威胁（threat）更受学界关注。然而，学界尚未就如何识别与衡量威胁，或是如何将领导人对威胁的感知系统性纳入研究模型达成共识。在本研究短论中，我们提出一种基于文本的策略与方法，用于从公开来源中识别并衡量精英群体对国际威胁的评估。借助半监督机器学习（semi-supervised machine learning）模型，我们展示了如何解析报纸文章中的文本，以辨别区分威胁性与非威胁性国家的论述，并衡量并追踪外国威胁强度随时间的变化。为验证概念可行性，我们使用1861年至2017年《纽约时报》（The New York Times）的新闻摘要，为美国构建了地缘政治威胁指数（Geopolitical Threat Index，GTI）。我们发现，该指数可与美国历史上被历史学家认定为高威胁与低威胁的时期相契合，且能准确识别出在不同历史时期对美国国家安全构成威胁的国家。此外，我们还将地缘政治威胁指数（GTI）与依赖物质能力衡量与国家间行为的传统国际威胁指标进行了对比分析。