Bayesian Models of Social Behavior using Online Resources

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People spend a great deal of their lives socializing, or interacting with other people. On a typical day a person might collaborate on a project with their work colleagues, play softball with their teammates, and converse with their family. Social interactions are inherently part of most of our activities, therefore, understanding social interactions is a fundamental part of understanding human behavior. As the amount of time people spend online rapidly grows, social interactions which were once limited to in-person meetings, letters, and telephone calls, are increasingly occurring through the use of online resources such as email, Facebook, and online chats. Social and cognitive scientists who strive to understand human behavior can analyze online interactions to illuminate social behavior in this new setting, and benefit from the wealth of data that it provides. However, social interactions are extremely complex, so analyzing and modeling them is not easy in any setting.Fortunately Bayesian probabilistic methods offer rich, flexible, generative models for data, which can be used to model complex, highly structured, social interactions. In general, Bayesian methods provide a principled framework for reasoning about an uncertain world. Bayesian latent variable models allow us to reason about, or discover, the potentially quite complex, unobserved structure that underlies what we do observe. This research develops methods which discover the unobserved structure necessary to model complex social interactions which occur online, explore group interactions, evaluate how context effects social interactions, and explore social influence. This work has the potential to improve science (e.g. by improving long-distance collaborations), commerce (e.g. by identifying whom businesses should inform about their products), and society at large (e.g. by improving social networking).