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

2020-01-22T19:31:20Z (GMT) by Hemank Lamba
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
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
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
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 behavior of actions taken at individual
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
recommend the next product? The second sub-part (ii) focusses on user-based phenomena, where multiple users are analyzed collectively to discover an interesting
phenomena, for example what are the characteristics of communication pattern 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 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
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
generated models to detect abnormal and potentially fraudulent behavior.