Modeling Social Diffusion Phenomena using Reality Mining

The diffusion of information, ideas, opinions and media in a social network and the influence of individual nodes on the diffusion process are important questions in the social sciences. However, till date, there has been no method to automatically capture fine-grained social interactions between people and utilize it to better model the diffusion process. In this paper, we describe the use of socially-aware mobile phones to capture face-to-face interactions and music diffusion for eighty-percent of the residents of an undergraduate dormitory. We show that observations of diffusion can be used as an `active probe’ to parse the network structure and the type of relationship between nodes more accurately than with ‘passive’ mobile sensor data alone. We propose that automatically captured social interactions can be used to create more accurate quantitative models of real-world diffusion and influence.