Abstract
Knowing what perpetrators want can inform strategies to achieve safe, secure, and sustainable societies. To help advance the body of knowledge in counterterrorism, this research applied natural language processing and machine learning techniques to a comprehensive database of terrorism events. A specially designed empirical topic modeling technique provided a machine-aided human decision process to glean six categories of perpetrator aims from the motive text narrative. Subsequently, six different machine learning models validated the aim categories based on the accuracy of their association with a different narrative field, the event summary. The ROC-AUC scores of the classification ranged from 86% to 93%. The Extreme Gradient Boosting model provided the best predictive performance. The intelligence community can use the identified aim categories to help understand the incentive structure of terrorist groups and customize strategies for dealing with them.
Highlights
Knowing the aims of terrorist attacks can help the intelligence community devise different strategies to deal with the perpetrators
The two subsections present the results of the empirical topic classification (ETC) and the machine learning (ML) classification to validate the accuracy of associating the summary narratives with perpetrator aim categories (PACs)
A comprehensive review of the research on terrorism revealed a dominant assumption that terrorists attack civilians to maximize political ends
Summary
Knowing the aims of terrorist attacks can help the intelligence community devise different strategies to deal with the perpetrators. The main goal of this research is to identify terrorist aims and classify them into a finite set of categories. This research defines aims of terrorists as distinctly different from the root causes of terrorism. Aims are the agenda or desired outcome of an attack (e.g., provoke a US attack upon Muslims, total war upon “non-believers”, and the creation of a global Caliphate promoted by Al-Qaeda) whereas root causes explain the birth of terrorists (e.g., the desire to establish the Islamic rule worldwide) (Burke 2021). The dominant paradigm of research in counterterrorism assumes that terrorists are rational actors that attack civilians for political ends (Géron 2017). As rational actors, terrorists must have an agenda or aim. Systemic models that attempt to explain aims involve a difficult tradeoff between generalizability and accuracy (Hastie et al 2016)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
