Detection of Terrorism-related Twitter Communities using Centrality Scores

ABSTRACT

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.

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Index Terms

• Human-centered computing

  • Collaborative and social computing

    • Collaborative and social computing systems and tools

      • Social networking sites

• Information systems

  • Information retrieval

    • Evaluation of retrieval results

      • Test collections

    • Specialized information retrieval

      • Multimedia and multimodal retrieval

  • World Wide Web

    • Web searching and information discovery

• Networks

  • Network types

    • Overlay and other logical network structures

      • Social media networks