Finding Online Extremists in Social Networks

Tuesday, April 10, 2018 - 3:00pm to 4:00pm

Event Calendar Category

LIDS Seminar Series

Speaker Name

Tauhid Zaman

Affiliation

MIT

Building and Room Number

32-141

Abstract

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.

Biography

Tauhid is an Assistant Professor of Operations Management at the MIT Sloan School of Management. He received his BS, MEng, and Ph.D. degrees in electrical engineering and computer science from MIT. His research focuses on solving operational problems involving social network data using probabilistic models, network algorithms, and modern statistical methods.  Some of the topics he studies in the social networks space include predicting the popularity of content, finding online extremists, and geo-locating users. His broader interests cover data-driven approaches to investing in startup companies, non-traditional choice modeling, algorithmic sports betting, and biometric data. His work has been featured in the Wallstreet Journal, Wired, Mashable, the LA Times, and Time Magazine.