Multi-modal Networks Reveal Patterns of Operational Similarity of Terrorist Organizations
This provides actionable insights for counter-terrorism and intelligence monitoring, addressing a previously lacking research area, but it is incremental as it applies a novel computational framework to existing data.
The study tackled the problem of detecting operational similarity among terrorist groups by analyzing their tactics, targets, and weapons from 1997 to 2018, finding increasing cohesiveness and high stability in similarity patterns from 2009 to 2018, with factors like activity and diversity driving similarity.
Capturing dynamics of operational similarity among terrorist groups is critical to provide actionable insights for counter-terrorism and intelligence monitoring. Yet, in spite of its theoretical and practical relevance, research addressing this problem is currently lacking. We tackle this problem proposing a novel computational framework for detecting clusters of terrorist groups sharing similar behaviors, focusing on groups' yearly repertoire of deployed tactics, attacked targets, and utilized weapons. Specifically considering those organizations that have plotted at least 50 attacks from 1997 to 2018, accounting for a total of 105 groups responsible for more than 42,000 events worldwide, we offer three sets of results. First, we show that over the years global terrorism has been characterized by increasing operational cohesiveness. Second, we highlight that year-to-year stability in co-clustering among groups has been particularly high from 2009 to 2018, indicating temporal consistency of similarity patterns in the last decade. Third, we demonstrate that operational similarity between two organizations is driven by three factors: (a) their overall activity; (b) the difference in the diversity of their operational repertoires; (c) the difference in a combined measure of diversity and activity. Groups' operational preferences, geographical homophily and ideological affinity have no consistent role in determining operational similarity.