Societal Computing Thesis Defense
- Gates Hillman Centers
- Reddy Conference Room 4405
- HEMANK LAMBA
- Ph.D. Student
- Ph.D. Program in Societal Computing
- Institute for Software Research, Carnegie Mellon University
Modeling User Behavior on Socio-Technical Systems Patterns and Anomalies
How can we model user behavior on social media platforms and social networking websites? How can we use such models to characterize and infer human behavior and preferences at scale? Specifically, how can we describe users that indulge in risk-taking behavior on social media or mobilize against a particular entity in a firestorm event on Twitter? Or how can we use such models to detect instances of fraudulent engagement?
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 dissertation, we focus on two broad topics - (a) understanding user behavior on social media platforms and (b) detecting fraudulent activities on these platforms. For the first part, we concentrate on user behavior in two different settings - (i) individual user behavior, where we analyze the behavior of actions taken at the individual scale, for example, modeling how does individual's expertise in e-commerce systems (such as wine rating, movie rating) evolve? and how can that be used to recommend the next product? The second sub-part (ii) focuses on user-based phenomena, where multiple users are analyzed collectively to discover an interesting phenomenon, for example, what are the characteristics of communication patterns between users participating in a firestorm event. In the second setting, we tackle the problem of detecting fraudulent activities on social media platforms. We solve two related sub-themes in the problem area: in the first sub-area, we characterize various fraudulent activities on social media platforms and propose anomaly detection models to identify them. For the next sub-area, we propose models that are not only confined to social media platformsbut can also be extended to other domains.
Overall, this thesis looks at two closely related problems i.e. modeling user behavior on social media platforms, and then using similarly generated models to detect abnormal and potentially fraudulent behavior.
Jürgen Pfeffer (Co-Chair)
Christos Faloutsos (Co-Chair)
J. Zico Kolter
Ceren Budak (University of Michigan)