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Information Dynamics in Social Interactions: Hidden Structure Discovery and Empirical Case Studies


As collective human activity and knowledge continues to be digitized and stored, it provides an unprecedented opportunity to understand information dynamics, how they evolve, and how individuals and organizations interact to form groups and make decisions. The petabytes of data collected everyday, however, underscores the need for new computational tools to help organize and understand these vast amounts of information. The focus of this dissertation has been to develop such tools, and present empirical case studies that both establish the efficacy of the developed tools, and provide new insights into the data sets themselves. For example, (i) We analyze a publicly accessible movie database and find global patterns in the underlying collaboration dynamics, and then show how such emergent patterns can be generated from stochastic decisions made at the level of the actors; (ii) We analyze the so called Twitter revolution that was precipitated by the 2009 elections in Iran, and determine a model for the spread of news on Twitter; (iii) We analyze the various aspects of online conversations and demonstrate how they are effective in revealing information dynamics; and finally, (iv) We develop a novel methodology for Topic Models, where given a large corpus of documents, it automatically infers the underlying topics and computes a distribution of words over the computed topics.

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