Project description
A new way to enhance counterterrorism efforts
Terrorist groups continuously adapt to changes, maintaining their relevance and power despite counterterrorism efforts. This adaptive capacity poses a significant challenge to global security, as traditional strategies often struggle to keep pace with evolving tactics and ideologies. In this context, the ERC-funded TERGAP project will develop a nuanced theoretical framework and use quantitative methods. Integrating political psychology, social movement, and terrorism research, the project uses big data analytics and machine learning to identify adaptation patterns. The project views terrorism as a recruitment tool, exploiting psychological needs like vengeance. TERGAP will test short-term strategic changes post-government repression using Event Coincidence Analysis. It will also collect data on counterterrorism policies and actions globally.
Objective
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.
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.
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 behaviours and build predictive models that anticipate adaptation. This has implications to improve counterterrorism and make it more proactive, focused, and effective.
Fields of science
- social sciencespolitical sciencespolitical transitionsterrorism
- natural sciencescomputer and information sciencesdata sciencebig data
- natural sciencescomputer and information sciencesdata sciencenatural language processing
- social sciencespolitical sciencespolitical transitionsarmed conflicts
- social sciencespolitical sciencespolitical transitionsrevolutions
Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Topic(s)
Funding Scheme
HORIZON-ERC - HORIZON ERC GrantsHost institution
2311 EZ Leiden
Netherlands