Detection of Terrorism-related Twitter Communities using Centrality Scores
Social media are widely used among terrorists to communicate and disseminate their activities. User-to-user interaction (e.g. mentions, follows) leads to the formation of complex networks, with topology that reveals key-players and key-communities in the terrorism domain. Both the administrators of social media platforms and Law Enforcement Agencies seek to identify not only single users but groups of terrorism-related users so that they can reduce the impact of their information exchange efforts. To this end, we propose a novel framework that combines community detection with key player identification to retrieve communities of terrorism-related
social media users. Experiments show that most of the members of each retrieved key-community are already suspended by Twitter, violating its terms, and are hence associated with terrorism-oriented content with high probability.