Semantic Similarity Measures in Newspaper Text for Detecting and Predicting Disruptive Institutional Events

This article proposes a semantic-similarity approach to detecting and predicting rare events in newspaper text, and applies it to institutional disruptions. Using a global news corpus covering more than 170 countries, we measure the similarity of headlines to event-specific prototypes in embedding space and aggregate these signals to identify disruptions to political institutions. We combine these text-based measures with supervised nowcasting and targeted human verification to expand existing datasets on military coups, irregular term-limit extensions, and weakening of the judiciary. The resulting event data are then used to forecast the likelihood of disruptions up to 12 months ahead, providing a high-frequency and scalable tool for monitoring institutional risk. As an illustration of its empirical value, we document that coups are followed by large and persistent declines in economic growth. More broadly, the framework can be adapted to detect and track a wide range of economic and political events and policy actions from news text in real time and in historical archives.