Gestalt Computing and the Study of Content-Oriented User Behavior on the Web
Aug 27, 2013
from 03:00 PM to 05:00 PM
|Where||Engr. IV Bldg., Maxwell Room 57-124|
|Contact Name||Roja Bandari|
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Advisor: Vwani P. Roychowdhury
Elementary actions online establish an individual’s existence on the web and his/her orientation toward different issues. In this sense, actions truly define a user in spaces like online forums and communities and the aggregate of elementary actions shape the atmosphere of these online spaces. This observation, coupled with the unprecedented scale and detail of data on user actions on the web, compels us to utilize them in understanding collective human behavior. Despite large investments by industry to capture this data and the expanding body of research on big data in academia, gaining insight into collective behavior online has been elusive. If one is indeed able to overcome the considerable computational challenges posed by both the scale and the inevitable noisiness of the associated data sets, one could provide new automated frameworks to extract insights into evolving behavior at different scales, and to form an altogether different perspective of aggregated elementary user actions.
This thesis addresses this fundamental and pressing problem and offers a gestalt computing approach when studying complex social phenomena in large datasets. This approach involves extracting macro structures from aggregated user actions, finding their possible meanings, and arranging data in layers so that it is iteratively explorable. The dissertation includes three major sections; first modeling and prediction of diffusion of information by users on the social web; next, detection of topics promoted by user communities; finally, presentation of the gestalt computing framework through a methodology that uses graph theory, language processing, and information theory to provide a top-down map of group dynamics on social news websites. What we find is not only statistical significance in the extracted structure, but also that the results are meaningful to human understanding. The efficacy of the proposed methodologies is established via multiple real-world data sets.
Roja Bandari is a Ph.D. candidate at the University of California, Los Angeles (UCLA), Department of Electrical Engineering. She received her B.S. and M.S. degrees in Electrical Engineering from UCLA in 2005 and 2007 and completed a graduate Gender Studies Concentration at UCLA in 2013. She is a recipient of the NSF Graduate Research Fellowship, and a departmental Excellence in Teaching award. In 2011 she interned as a research associate at Hewlett Packard Labs and in 2012 and 2013 she served as an academic mentor at the Institute for Pure and Applied Mathematics (IPAM). Her research interests are in complex networks, data mining, and computational social sciences.