This project will develop a hybrid method for rigorously observing structures of social interaction over time, and validate this method by comparison with conventional survey and observation designs. It will use both wearable and fixed computer devices to collect streaming data on research participants' physical location, speech, and motion, and then will develop computational models to infer structures of social interaction from these data. This suite of tools will thus allow direct automated measurement of networks of face-to-face interaction over time. Having demonstrated and validated this approach, the project will illuminate a set of classic theoretical problems that have eluded rigorous analysis under conventional methods. Substantial advances in modeling the dynamics of social networks have been frustrated by the paucity of appropriate data for empirical investigation, as scholars must often address dynamic theories using cross-sectional or sparse panel data.
The team of investigators includes experts from both Computer Science and Sociology, integrates tools from both fields, and addresses questions that would be intractable without this interdisciplinary lens. For example, the precise measurement of interaction in time and space allows researchers to observe the co-evolution of social roles (as performed by individuals in day-to-day interaction) and structural positions in a social network. The streaming measures of social interaction allow a detailed analysis of conversations, analyzing how styles of communication change within social relationships over time, including the effect of structural position on styles of interaction and the effect of interaction style on position in the network.
The research will examine the simple evolution of social networks over short (weeks) and long (month and/or years) time scales. Using Global Positioning Systems and various other location sensor technologies, the work will contribute an explicitly spatial investigation of network dynamics, modeling the interplay of the physical environment and social networks. For example, particular locations may serve as hubs or bridges, connecting otherwise disparate network components. Results may refine scientific understanding of the co-evolution of social networks and physical locations.
The project will develop a set of methods for social network observation and analysis, generate datasets of unprecedented breadth and depth, and provide an independent standard for comparison of conventional tools, all of which will be invaluable resources for the broader scientific community. The resulting longitudinal network datasets are likely to be mined for insights into social network dynamics by many other researchers, while the team of graduate students working under this project will benefit from unique interdisciplinary training. Beyond basic research, the novel application of sensor-based and machine learning methods to understanding human communication has broad applicability to real-world social problems. As a simple example, a refined understanding of the co-evolution of networks and physical locations may provide insight into macro-level processes of community integration and disintegration, informing social architects and urban planners. The project will promote teaching, training and understanding among researchers in computer science and social science.
