Insights from Social Psychology to Computational User Modeling

Lin Gong
Department of Computer Science
University of Virginia

Abstract The advent of participatory web has created massive amounts of user-generated data, which enables the study of online user attributes and behaviors. Traditional social psychology studies commonly conduct surveys and experiments to collect user data in order to infer attributes of individuals, which are expensive and time-consuming. In contrast, we aim to understand users by building computational user models automatically, thereby to save time and efforts. And the principles of social psychology serve as good references for building such computational models.

In this presentation, I will discuss about my PhD works for modeling online user behaviors based on user-generated data. In particular, two challenges are addressed: (1) modeling users’ diverse ways of expressing attitudes or opinions; (2) building unified user models by integration of different modalities of user-generated data. This presentation bridges the gap between social psychology and computation behavior modeling which also provides a foundation for making user behavior modeling useful for many other applications.