Most notably we find that Leveraging the multiplex and multinode structures

Most notably we find that leveraging the multiplex

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Most notably, we find that: Leveraging the multiplex and multinode structures available in Twitter data significantly improved our algorithm’s ability to accurately identify ISIS OEC members on Twitter. Identifying and isolating large portions of an online extremist community offers unique insights into the group’s ideology and influence, and helps identify key users and roles. IVCC offers promise for making online extremist community detection in social media a practical reality to inform both diplomacy and defense initiatives. This case study offers a unique opportunity to infer positively labelled cases based on Twitter suspensions and clustering techniques. However, it is unlikely that such a large number of labeled cases would always be available. Thus, implementations using semi-supervised algorithms or active learning [ 70 ] would make IVCC more generalizable, and should be a topic for future research. IVCC is also limited in that it does not account for simultaneous group membership of users. It is likely that there are jihadists that support various terrorist groups and allegiances can be dynamic. IVCC does not provide probabilistic clustering or account for changes in group dynamics over time. Similar to [ 71 ], we would like to extend this methodology to an overlapping group framework to account for these types of users and also explore methods to identify temporal change points. Finally, though preliminary results for IVCC as a methodology are encouraging, they are limited in that we do not provide an empirical analysis of IVCC with respect to benchmark. We will leave this analysis to future work, due to the emphasis of this paper being the ISIS case study.
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  • Fall '17

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