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.
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.