Thesis Proposal : Online Extremist Community Detection , Analysis , and Intervention

The rise of the Islamic State of Iraq and al-Sham (ISIS) has been watched by millions through the lens of social media. This “crowd” of social media users has given the group broad reach resulting in a massive online support community that is an essential element of their public affairs and resourcing strategies. Other extremist groups have begun to leverage social media as well. Online Extremist community (OEC) detection holds great potential to enable social media intelligence (SOCMINT) mining as well as informed strategies to disrupt these online communities. I present Iterative Vertex Clustering and Classification (IVCC), a scalable analytic approach for OGTSC detection in annotated heterogeneous networks, and propose several extensions to this methodology to help provide policy makers the ability to identify these communities as scale, understand their interests, and shape policy decisions. In this thesis, I propose contributions to OEC detection, analysis, and disruption: • efficient identification of positive case examples through semi-supervised dense community detection • monitoring dynamic OECs through a repeatable search and detection methodology • gaining influence within OECs through topologically derived @mention strategies • an extended literature review of methods applicable to detection, analysis, and disruption of OECs The contributions proposed in this thesis will be applied to four large Twitter corpora containing distinct online communities of interest. My goal is to provide a substantive foundation enabling follow on work in this emergent area so critical to counter-terrorism and national security.