Thu, Mar 21, 2019 @ 11:00 AM - 12:00 PM
Conferences, Lectures, & Seminars
Speaker: Srijan Kumar, Stanford University
Talk Title: Data Science for Healthy Online Interactions
Series: CS Colloquium
Abstract: The web enables users to interact with one another and shape opinion at an unprecedented speed and scale. However, the prevalence of disinformation and malicious users makes the web unsafe and unreliable, for example, 40% of users have experienced online harassment and platforms have disabled user comments because of trolling. In this talk, I will present data science methods that help us to create a better and safer web ecosystem for everyone. In particular, I will present methods to extract knowledge from the social graph structure and augment with behavior signals to characterize, detect, and mitigate the damage of disinformation and malicious users.
First, I will describe a graph mining collective classification algorithm to identify fake reviews on e-commerce platforms. The method learns trustworthiness scores from the user-to-product review network to identify sophisticated fraudsters. The method is currently being used in production at Flipkart, India's largest e-commerce platform. Next, I will present the first web-scale characterization of multiple account abuse in online discussions and my method of statistical analysis of user interaction graphs to detect them. Finally, I will show how learning embeddings from the social network structure helps to predict online conflicts and to mitigate their damage. These methods power online tools that help administrators in Reddit and Wikipedia.
I will conclude the talk by describing my future research directions that will enable us to proactively predict how malicious behavior will evolve in the future, both on web platforms and face-to-face interactions
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Srijan Kumar (https://stanford.edu/~srijan/) is a postdoctoral scholar in Computer Science at Stanford University. His research investigates data science and machine learning to create healthy online and offline interactions, focusing on developing methods to curb deception, misbehavior, and disinformation. His methods have had a tangible real-world impact and are being used at major tech companies, including Flipkart, Reddit, and Wikipedia. His research has received the ACM SIGKDD Doctoral Dissertation Award runner-up 2018, Larry S. Davis Doctoral Dissertation Award 2018, and WWW Best Paper Award runner-up 2017. His research is interdisciplinary and has been included in the curriculum at several universities, including UIUC, University of Michigan, and Stanford University. His research has been included in documentary (Familiar Shapes) and covered in popular press, including CNN, The Wall Street Journal, Wired, and New York Magazine. He did his Ph.D. in Computer Science from University of Maryland, College Park, and B.Tech. from Indian Institute of Technology, Kharagpur.
Host: Xiang Ren
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: Assistant to CS chair