Country-level news corpus coverage

Terrorist groups often survive by adapting to counterterrorism pressures. Yet we lack comprehensive cross-national data to assess when, how, and with what consequences that adaptation occurs. The Terrorist Group Adaptation & Lessons for Counterterrorism (TERGAP) project addresses this gap by building a new dataset of counterterrorism events. It will help researchers link terrorism and counterterrorism data, identify patterns of adaptation, examine the unintended effects of counterterrorism, and generate evidence to improve global security.

The project began with human coding of news articles available through Leiden University's NexisUni subscription. In its second stage, featured on this webpage, the research team uses the LexisNexis API to identify articles matching a defined set of counterterrorism search terms. Using this article corpus, machine-learning tools and LLMs identify relevant information (who did what, to whom) and detect and extract counterterrorism events. Human coders then validate these events to ensure the resulting data are accurate and reliable.