•   When: Thursday, February 27, 2020 from 02:00 PM to 03:00 PM
•   Speakers: Hemank Lamba
•   Location: Engineering Building 4201
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Abstract:

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 how we can apply data science methods to propose an overall framework for uncovering the entire pipeline of fraud on these platforms - and furthermore, how can we model user behavior on these platforms that could be efficiently used to detect other forms of deviant behavior.
In the end, I will discuss how can insights derived from the work can be used by platform designers to redesign or carry out necessary interventions.



Bio:
Hemank Lamba is a recently graduated PhD from 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.