  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.
