Abstract
Social networks are currently the main medium through which terrorist organisations reach out to vulnerable people with the objective of radicalizing and recruiting them to commit violent acts of terrorism. Fortunately, radicalization on social networks has warning signals and indicators that can be detected at the early stages of the radicalization process. In this article, we explore the use of the semantic web and domain ontologies to automatically mine the radicalisation indicators from messages and posts on social networks. Specifically, we propose an ontology for the radicalisation domain as well as an approach to automatically compute the radicalisation indicators. In our approach, social messages are annotated with concepts and instances defined formally in a domain ontology. Annotations are then exploited within an inference phase to identify the messages exhibiting a radicalization indicator. The indicators are then computed using a set of SPARQL queries. We have implemented and evaluated the proposed solution based on a pubic Tweet dataset. Obtained results show the effectiveness of our approach. The article presents also the implemented prototype.
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