TR#564: Sensing and Modeling Human Networks using the Sociometer

Tanzeem Choudhury and Alex Pentland

Knowledge of how people interact is important in many disciplines, e.g. organizational behavior, social network analysis, information diffusion and knowledge management applications We are developing methods to automatically and unobtrusively learn the social network structures that arises within  human groups based on wearable sensors. At present researchers mainly have to rely on questionnaires, surveys or diaries in order to obtain data on physical interactions between people. In this paper, we show how sensor measurements from the sociometer can be used to build computational models of group interactions. We present results on how we can learn the structure of face-to-face interactions within groups, detect when members are in face-to-face proximity and also when they are having a conversation. The questions we are exploring are: Can we tell who influences whom? Can we quantify this amount of influence?  How can we modify group interactions to promote better information diffusion?   

Project Page: Shortcuts: Creating Small Worlds

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