Terrorist Group Adaptation & Lessons for Counterterrorism

This project develops a strategic framework using big data and machine learning to analyze terrorist group adaptations to repression and counterterrorism, aiming to enhance proactive counterterrorism efforts.

Subsidie
€ 1.500.000
2024

Projectdetails

Introduction

Terrorist groups find ways to adapt to changes in their environment to stay relevant and powerful. This project offers new insights into this phenomenon by developing a more nuanced theoretical strategic framework and using quantitative methods to examine how terrorist groups survive, and sometimes thrive, despite efforts to combat them. This is accomplished by integrating political psychology, social movement, and terrorism research, and applying big data analytics and machine learning common in brain sciences, natural sciences, and bioinformatics to identify adaptation patterns in terrorist attack target selection and brutality.

Terrorism as a Recruitment Tool

First, this project frames terrorism as a recruitment tool for manipulating potential supporters’ psychological needs, like vengeance. Repressive government actions lead to desires for vengeance and thus create opportunities for acts of terrorism specifically attacking the repressive actor to signal a terrorist group’s capability for fulfilling this psychological need.

As such, we should observe strategic short-term changes in terrorism following government repression in the data. This is tested using Event Coincidence Analysis, a method for identifying synchronization patterns and trigger rates from one event to another.

Adaptation to Counterterrorism

Second, because terrorist groups can also adapt to changes in counterterrorism, this project proposes two data collection efforts that enable big data analytics to identify adaptation patterns:

  1. The first focuses on counterterrorism policies using government reports and covers a global sample of countries.
  2. The second creates a novel large-N cross-national counter-terrorist actions dataset using natural language processing machine coding of news articles.

Hierarchical clustering analyses will then be used to detect patterns of terrorist group adaptive behaviors and build predictive models that anticipate adaptation. This has implications to improve counterterrorism and make it more proactive, focused, and effective.

Financiële details & Tijdlijn

Financiële details

Subsidiebedrag€ 1.500.000
Totale projectbegroting€ 1.500.000

Tijdlijn

Startdatum1-1-2024
Einddatum31-12-2028
Subsidiejaar2024

Partners & Locaties

Projectpartners

  • UNIVERSITEIT LEIDENpenvoerder

Land(en)

Netherlands

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