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.

中文翻译：

---

地缘政治威胁指数：识别外国威胁的基于文本的计算方法

在国际政治研究中，几乎没有什么概念比威胁更重要。然而，学者们在如何识别和衡量威胁或系统地将领导者对威胁的看法纳入他​​们的模型中并没有达成一致意见。在本研究报告中，我们介绍了一种基于文本的策略和方法，用于从公开来源中识别和衡量精英对国际威胁的评估。使用半监督机器学习模型，我们展示了如何解析来自报纸文章的文本，以辨别区分威胁和非威胁状态的论点，并测量和跟踪外国威胁强度随时间的变化。为了证明概念验证，我们使用《纽约时报》从 1861 年到 2017 年的新闻摘要来创建美国的地缘政治威胁指数 (GTI)。我们表明，该指数成功地匹配了美国历史上被历史学家识别为高威胁和低威胁的时期，并正确识别了在其历史不同时期对美国安全构成威胁的国家。我们将 GTI 与依赖物质能力和州际行为衡量的传统国际威胁指标进行比较和对比。