New technologies have made it possible to collect information about social networks as they are acted and observed in the wild , instead of as they are reported in retrospective surveys. These technologies offer opportunities to address many new research questions: How can meaningful information about social interaction be extracted from automatically recorded raw data on human behavior? What can we learn about social networks from such fine-grained behavioral data? And how can all of this be done while protecting privacy? With the goal of addressing these questions, this article presents new methods for inferring colocation and conversation networks from privacy-sensitive audio. These methods are applied in a study of face-to-face interactions among 24 students in a graduate school cohort during an academic year. The resulting analysis shows that networks derived from colocation and conversation inferences are quite different. This distinction can inform future research in computational social science, especially work that only measures colocation or employs colocation data as a proxy for conversation networks.

The current methods used to mine and analyze temporal social network data make two assumptions: all edges have the same strength, and all parameters are time-homogeneous. We show that those assumptions may not hold for social networks and propose an alternative model with two novel aspects: (1) the modeling of edges as multi-valued variables that can change in intensity, and (2) the use of a curved exponential family framework to capture time-inhomogeneous properties while retaining a parsimonious and interpretable model. We show that our model outperforms traditional models on two real-world social network data sets.

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.

The UW dynamic social network study is an effort to automatically observe and model the creation and evolution of a social network formed through spontaneous face-to-face conversations. We have collected more than 4,400 hours of data that capture the real world interactions between 24 subjects over a period of 9 months. The data was recorded in completely unconstrained and natural conditions, but was collected in a manner that protects the privacy of both study participants and non-participants. Despite the privacy constraints, the data allows for many different types of inference that are in turn useful for studying the prosodic and paralinguistic features of truly spontaneous speech across many subjects and over an extended period of time. This paper describes the new challenges and opportunities presented in such a study, our data collection effort, the problems we encountered, and the resulting corpus.

We present privacy-sensitive methods for (1) automatically finding multi-person conversations in spontaneous, situated speech data and (2) segmenting those conversations into speaker turns. The methods protect privacy through a feature set that is rich enough to capture conversational styles and dynamics, but not sufficient for reconstructing intelligible speech. Experimental results show that the conversation finding method outperforms earlier approaches and that the speaker segmentation method is a significant improvement to the only other known privacy-sensitive method for speaker segmentation. Index Terms: conversation modeling, speaker diarization, privacy, context-aware computing