Finding Online Extremists in Social Networks

Online extremists in social networks pose a new form of threat to the general public.  These extremists range from cyber bullies who harass innocent users to terrorist organizations such as ISIS that use social networks to spread propaganda.  Currently, social networks suspend the accounts of such extremists in response to user complaints, but these extremist users simply create new accounts and continue their activities.  In this talk, we present a new set of operational capabilities to help authorities mitigate the threat posed by online extremist groups in social networks.

Using data from several hundred thousand extremist accounts on Twitter, we develop a behavioral model for these users, in particular, what their accounts look like and who they connect with.  This model is used to identify new extremist accounts by predicting if they will be suspended for extremist activity.  We also use this model to track existing extremist users as they create new accounts by identifying if two accounts belong to the same user.  Finally, we use this model as the basis for an efficient policy to search the social network for suspended users’ new accounts. Our search approach is based on a variant of the classic Polya’s urn setup.  We find a simple characterization of the optimal search policy for this model under fairly general conditions. Our search policy and main theoretical results generalize easily to search problems in other fields. 

Joint work with Jytte Klausen and Christopher Marks.