Abstract

Knowledge of how groups of people interact is important in many disciplines, e.g. organizational behavior, social network analysis, knowledge management and ubiquitous computing. Existing studies of social network interactions have either been restricted to online communities, where unambiguous measurements about how people interact can be obtained, or have been forced to rely on questionnaires, or diaries to get data on face-to-face interactions. Survey-based methods are error prone and impractical to scale up. This paper describes our work in developing a computational framework to model face-to-face interactions within a community. We have integrated methods from speech processing and machine learning to demonstrate that it is possible to extract information about people's patterns of communication, without imposing any restriction on the user's interactions or environment. Furthermore, we analyze some of the conversational dynamics and present results that demonstrate distinctive and consistent turn-taking styles for individuals during conversations. Finally, we present results that show strong correlation between a person's turn-taking style during one-on-one conversations and the person's role within the network.

Citations