Description du projet
Une nouvelle façon de renforcer les efforts de lutte contre le terrorisme
Les groupes terroristes s’adaptent constamment aux changements, conservant leur pertinence et leur pouvoir malgré les efforts de lutte contre le terrorisme. Cette capacité d’adaptation représente un défi important pour la sécurité mondiale, car les stratégies traditionnelles peinent souvent à suivre le rythme de l’évolution des tactiques et des idéologies. Dans ce contexte, le projet TERGAP, financé par le CER, développera un cadre théorique nuancé et utilisera des méthodes quantitatives. Intégrant la psychologie politique, les mouvements sociaux et la recherche sur le terrorisme, le projet recourt à l’analyse de données massives et à l’apprentissage automatique pour identifier les modèles d’adaptation. Le projet considère le terrorisme comme un outil de recrutement, qui exploite des besoins psychologiques comme la vengeance. TERGAP testera les changements stratégiques à court terme après la répression gouvernementale en utilisant l’analyse des coïncidences d’événements. Il recueillera également des données sur les politiques et les actions antiterroristes menées au niveau mondial.
Objectif
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.
Champ scientifique
- 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
Thème(s)
Régime de financement
HORIZON-ERC - HORIZON ERC GrantsInstitution d’accueil
2311 EZ Leiden
Pays-Bas