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Spoken Networks: Analyzing Face-To-Face Conversations and How They Shape Our Social Connections

01 Mar
Monday, 03/01/2010 6:00am to 7:00am

Tanzeem Choudhury
Computer Science

Computer Science Building, Rooms 150 & 151

Faculty Host: Deepak Ganesan

With the proliferation of sensor-rich mobile devices, it is becoming increasingly easy to collect data that capture the real-world social interactions of entire groups of people. These new data sets provide opportunities to study the social networks of people as they are observed "in the wild." However, the traditional methods of social network analysis are often inadequate for such behavioral data. Most existing techniques apply only to static, binary data. Social networks derived from behavioral data are almost always temporal and are often non-stationary and have finer grained observations about interactions as opposed to simple binary indicators. Thus, new techniques are needed that can take into account variable tie intensities and the dynamics of a network as it evolves in time. In this talk, I will provide an overview of the computational framework we have developed for modeling the micro-level dynamics of human interactions as well as the macro-level network structure and its dynamics from local, noisy sensor observations. Furthermore, by studying the micro and macro levels simultaneously we are able to link dyad-level interaction dynamics (local behavior) to network-level prominence (a global property). I will conclude by providing some specific examples of how the methods we have developed can be applied more broadly to problems in computational social science.

Based on joint work with Danny Wyatt (UW), James Kitts (Columbia), Jeff Bilmes (UW), Andrew Campbell (Dartmouth), and Ethan Berke (Dartmouth Medical School)