|Institution:||University of Arizona|
|Keywords:||Social Media; Social Networks; Text Mining; Twitter; Wikipedia; Management; Crowd Sourcing|
|Full text PDF:||http://hdl.handle.net/10150/620677|
The beginning of this decade has seen a phenomenal raise in the amount of data generated in the world. While this increase provides us with opportunities to understand various aspects of human behavior and mechanisms behind new phenomena, the technologies, statistical techniques and theories required to gain an in depth and comprehensive understanding haven't progressed at an equal pace. As little as 5 years back, we used to deal with problems where there is insufficient prior social science or economic theory and the interest is only in prediction of the outcome or where there is an appropriate social science or economic theory and the interest is in explaining a given phenomenon. Today, we deal with problems where there is insufficient social science or economic theory but the interest is in explaining a given phenomenon. This creates a big challenge the solution to which is of equal interest to both academics and practitioners. In my research, I contribute towards addressing these challenges by building exploratory frameworks that leverage a variety of techniques including social network analysis, text and data mining, econometrics, statistical computing and visualization. My three essay dissertation focuses on understanding the antecedents to the quality of user generated content and on subscription and un-subscription behavior of users from lists on Social Media. Using a data science approach on population sized samples from Wikipedia and Twitter, I demonstrate the power of customized exploratory analyses in uncovering facts that social science or economic theory doesn't dictate and show how metrics from these analyses can be used to build prediction models with higher accuracy. I also demonstrate a method for combining exploration, prediction and explanatory modeling and propose to extend this methodology to provide causal inference. This dissertation has general implications for building better predictive and explanatory models and for mining text efficiently in Social Media. Advisors/Committee Members: Ram, Sudha (advisor), Bockstedt, Jesse (committeemember).