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.
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:
- The first focuses on counterterrorism policies using government reports and covers a global sample of countries.
- 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
Startdatum | 1-1-2024 |
Einddatum | 31-12-2028 |
Subsidiejaar | 2024 |
Partners & Locaties
Projectpartners
- UNIVERSITEIT LEIDENpenvoerder
Land(en)
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Collective Adaptation
This project aims to develop a scientific paradigm for studying collective adaptation by integrating cognitive and social processes through computational models and empirical data to address societal challenges.
Countering Jihadi Insurgencies in Africa: Repress, Resist & Reorder
COUNTERRR aims to analyze domestic responses to jihadist armed groups in Mali, Nigeria, and Mozambique to enhance international strategies for conflict stabilization and prevention.
Towards an evidence-based model for big data policing: Evaluating the statistical-methodological, criminological and legal and ethical conditions
This project aims to develop an interdisciplinary, evidence-based model for big data policing in Europe, addressing research gaps and enhancing crime prediction and resource allocation through randomized trials.
Understanding the Processes Underlying Societal Threats using Novel Cluster-based Methods
The project aims to develop a mixture multigroup structural equation modeling framework to accurately analyze the drivers of polarized beliefs across diverse groups, addressing measurement challenges.
Comprehending Human Action using Social Networks
This project explores how social networks influence the behavior of scientists, politicians, and citizens through empirical analyses and network-based identification strategies.
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