Detecting online recruitment of terrorists: towards smarter solutions to counter terrorism

Abstract

Increased usage of social media led to formation of online communities of terrorist groups for discussing many violent plans. These online communities present big data to researchers to identify hidden patterns and behaviors to generate actionable intelligence which can be useful for security agencies. Recruiting new people over online social media is on emerging trend which presents how internet is exploited by terrorist groups. In this paper, we present automatic model to detect online cyber recruitment over social media. Online discussions regarding recruitment of terrorists or building new connections for recruitment of violent extremist have been labelled. Two experts labelled 730 messages from five dark web discussion forums named ‘Ansar1’, ‘Gawaher’, ‘Islamic Awakening’, ‘Islamic Network’ and ‘Mywic’ as YES (recruitment) and NO (non-recruitment). Statistical analysis has been done on two independent labelled messages to find mutual agreement between two judges. Kohen’s Kappa coefficient computed is 0.87 at p = 0.01 which signifies higher mutual agreement. Five machine learning classifiers namely support vector machine, logic boosting, random forest, generalized linear model and maximum entropy based model are developed to further classify recruitment labels. To the best of our knowledge no such work has been done by collaborating data from multiple dark web discussion forums to automatic identify online recruitment of terrorists. Our proposed model presents smart solution with usage of computational techniques to quantify terrorist behavior analysis and detect online recruitment of violent extremists over online social media and dark web forums.