Descrizione del progetto
Un nuovo modo per potenziare gli sforzi antiterrorismo
I gruppi terroristici si adattano continuamente ai cambiamenti, mantenendo la loro rilevanza e il proprio potere nonostante gli sforzi antiterrorismo che vengono compiuti. Questa capacità di adattamento rappresenta una sfida significativa per la sicurezza globale, poiché le strategie tradizionali spesso faticano a tenere il passo con l’evoluzione delle tattiche e delle ideologie del terrorismo. In questo contesto, il progetto TERGAP, finanziato dal CER, svilupperà un quadro teorico sfumato e utilizzerà metodi quantitativi. Integrando psicologia politica, movimenti sociali e ricerca sul terrorismo, il progetto si avvale dell’analisi dei megadati e dell’apprendimento automatico per identificare i modelli di adattamento. TERGAP considera il terrorismo come strumento di reclutamento, che sfrutta bisogni psicologici come la vendetta. Il progetto verificherà i cambiamenti strategici a breve termine in seguito alla repressione governativa mediante l’impiego di una tecnica nota come analisi di coincidenza degli eventi, raccogliendo inoltre dati sulle politiche e le azioni antiterrorismo a livello globale.
Obiettivo
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.
Campo scientifico
- 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
Parole chiave
Programma(i)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Argomento(i)
Meccanismo di finanziamento
HORIZON-ERC - HORIZON ERC GrantsIstituzione ospitante
2311 EZ Leiden
Paesi Bassi