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 behavior on social media<br>and infer about human behavior and preferences at scale? Specifically, how can we describe users that indulge in posting about risk-taking behavior on social media or<br>mobilize against a particular entity in a firestorm event on Twitter? 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<br>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<br>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<br>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<br>and also detect malicious entities at such a large scale.<br>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.<br>For the first part, we concentrate on user behavior in two different settings - (i) individual user behavior, where we analyze behavior of actions taken at individual<br>scale for example modeling how does individual’s expertise in e-commerce systems (such as wine rating, movie rating) evolve over time? and how can that be used to<br>recommend the next product? The second sub-part (ii) focusses on user-based phenomena, where multiple users are analyzed collectively to discover an interesting<br>phenomena, for example what are the characteristics of communication pattern between users participating in a firestorm event. In the second setting, we tackle the<br>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<br>fraudulent activities on social media platforms and propose anomaly detection models to identify fraudulent users and activities. For the next sub-area we propose models that are not only confined to social media platforms, but can also be<br>extended to general settings. Overall, this thesis looks at two closely related problems i.e. modeling user behavior on social media platforms, and then using similarly<br>generated models to detect abnormal and potentially fraudulent behavior.