TL;DR
This study introduces a novel computational framework to analyze and cluster terrorist groups based on their operational behaviors, revealing increasing cohesion and stability in their tactics over time.
Contribution
The paper presents a new multi-modal network approach to detect clusters of terrorist groups sharing operational patterns, emphasizing temporal stability and key behavioral factors.
Findings
Global terrorism shows increasing operational cohesiveness over time.
High year-to-year stability in group similarity from 2009 to 2018.
Operational similarity is mainly driven by activity levels and repertoire diversity.
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
