Topic-based social network analysis for virtual communities of interests in the Dark Web

The study of extremist groups and their interaction is a crucial task in order to maintain homeland security and peace. Tools such as social networks analysis and text mining have contributed to the understanding of this kind of groups in order to develop counter-terrorism applications. This work addresses the topic-based community key members extraction problem, for which our method combines both text mining and social network analysis techniques. This is achieved by first applying latent Dirichlet allocation to build two topic-based social networks: one social network oriented towards the thread creator point-of-view, and the other one oriented towards the repliers of the overall forum. Then, by using different Social Network Analysis measures, topic-based key members are evaluated using as benchmark a social network built using the plain documents. Experiments were performed using an English language based forum available in the Dark Web portal.

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