Modeling User Behavior on Socio-Technical Systems: Patterns and Anomalies

Thursday, February 13, 2020 - 11:00
Blakeslee Room
Hemank Lamba

Online social network platforms (e.g. Facebook, Twitter, Snapchat, Yelp) provide means for users to express themselves, by posting content in the form of images and videos. These platforms allow users to not only interact with content (liking, commenting) but also to other users (social connections, chatting) and items (through ratings and reviews), thus providing rich data with huge potential for mining unexplored and useful patterns. The availability of such data opens up unique opportunities to understand and model nuances of how users interact with such socio-technical systems, while also contributing novel algorithms that can predict genuine user behavior and also detect malicious entities at such a large scale.

In this talk, I will give an overview of the work done as part of my thesis. Specifically I will focus on two broad topics - (a) understanding user behavior on social media platforms and (b) detecting fraudulent activities on these platforms. I will discuss why it is necessary to model observed user behavior on large scale web platforms, and how they can be leveraged for furthering computational social science and detecting malicious entities. Furthermore, I will discuss how can insights derived from the work can be used by platform designers to redesign or carry out necessary interventions.


Hemank Lamba is a PhD student in the School of Computer Science at Carnegie Mellon University. His research is focused on understanding and modeling the user behavior on social media - specifically characterizing the deviant user behavior on these platforms, and understanding the effects of such behavior on the society. Hemank has published more than 30 articles in peer reviewed conferences and journals, winning Best Paper awards at ASONAM and SDM. Hemank is also recipient of CMU Presidential Fellowship and Snap Fellowship. Previously, he was a software engineer at IBM Research. He has also been a fellow with multiple Data Science for Social Good initiatives (University of Chicago and IBM Research), where he has tackled problems related to food insecurity in U.S. and understanding the ecospace of philanthropic projects. Hemank holds a B.Tech in Computer Science from IIIT-Delhi, India and a Masters in Machine Learning from Carnegie Mellon University.