Towards the Automated Social Analysis of Situated Speech Data

We present an automated approach for studying fine-grained details of social interaction and relationships. Specifically, we analyze the conversational characteristics of a group of 24 individuals over a six-month period, explore the relationship between conversational dynamics and network position, and identify behavioral correlates of tie strengths within a network. The ability to study conversational dynamics and social networks over long time scales, and to investigate their interplay with rigor, objectivity, and transparency will complement the traditional methods for scientific inquiry into social dynamics. They may also enable socially aware ubiquitous computing systems that are cognizant of and responsive to the user's engagement with her social environment.