The ease of use of the Internet has enabled violent extremists such as the Islamic State of Iraq and Syria (ISIS) to easily reach large audience, build personal relationships and increase recruitment. Social media are primarily based on the reports they receive from their own users to mitigate the problem. Despite efforts of social media in suspending many accounts, this solution is not guaranteed to be effective, because not all extremists are caught this way, or they can simply return with another account or migrate to other social networks. In this paper, we design an automatic detection scheme that using as little as three groups of information related to usernames, profile, and textual content of users, determines whether or not a given username belongs to an extremist user. We first demonstrate that extremists are inclined to adopt usernames that are similar to the ones that their like-minded have adopted in the past. We then propose a detection framework that deploys features which are highly indicative of potential online extremism. Results on a real-world ISIS-related dataset from Twitter demonstrate the effectiveness of the methodology in identifying extremist users.
Beyond these works, the work of @cite_3 takes a different approach to track individual's behavioral indicators of homegrown extremism, using public and law enforcement data. The intuition is to use graph pattern matching to identify suspicious trajectories and potential radicalization over a dynamic heterogeneous graph associated with the fused data from public and law enforcement. The authors first develop a query pattern of radicalization and then run several graph pattern matching algorithms to detect and track the on-going radicalization. They develop the investigative simulation graph pattern matching technique, which is composed of required extension to the existing dual simulation graph pattern matching method to avoid over-matching. This approach provides analysts and law enforcement officials with the ability to find partial full matches, given a query of radicalization, as well as the pace of the appearance of the radicalized extremists. As opposed to the above studies, in this paper, we make the first attempt on determining if a given Twitter handle belongs to an extremist user or not, using only little information gathered from the handle, profile and content.
@cite_3
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This paper outlines our on-going efforts to address the radicalization detection problem, the automated or semi-automated task of dynamically detecting and tracking behavioral changes in individuals who undergo the process of increasingly espousing jihadist beliefs and transition to the use of violent action in support of those beliefs. Leveraging the notion that personal trajectories towards violent radicalization exist, we take a graph pattern matching approach to track individual-level indicators using data fused from available public and government law enforcement databases. We show that our approach provides analysts with the ability to find full or partial matches against a query pattern of radicalization, and a means to quantify the pace of the appearance of the indicators that may help prioritize investigative efforts and resources to prevent planned attacks.