Mapping How Violent Groups Act Among Themselves, in their Communities, and with Rivals
Project Summary
The effectiveness of counter-extremism and counterterrorism policies can depend on understanding the characteristics of adversarial organizations — their leadership structure, relations with a local community, choice of tactics and targets, and origin story. It’s also vital to know how groups interact with each other. This research project describes a host of violent extremist organizations and maps their activities across the globe in a new website, reports, and journal articles.
Purpose/Objectives
This year, the project utilizes extremist organization profiles to describe the characteristics of violent adversarial organizations and their interactions with each other, with a focus on rivalries, to determine the level of threat they pose.
Method
The project methodology is two-fold: group profiles and relational maps. The profiles are developed through reviewing publicly available material and provide information on organizational structure, strategy, and interactions with competing violent extremist organizations. The data will be used to create visualizations to demonstrate relationships with other violent extremist organizations. Researchers will create genealogical maps and network diagrams of interactions among groups in specified conflict ecosystems.
Outputs and Impact
- Group profiles, new maps, new Mapping Militants website
- Series of reports and journal articles
Martha Crenshaw, Ph.D.
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Kaitlyn Robinson, Ph.D.
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Publications
- Transnational Ties Between Selected U.S. and Foreign Violent Extremist Actors: Evidence from the Mapping Militants Project
- Militant Splinter Groups and the Use of Violence
- Countering Far-Right Anti-Government Extremism in the United States
- Fighting the Hydra: Combatting Vulnerabilities in Online Leaderless Resistance Networks
- Emerging Risks in the Marine Transportation System (MTS), 2001- 2021
- Predicting Targeted Violence: An Update
- Predicting Domestic Extremism and Targeted Violence: A Machine Learning Approach