Computable social patterns from sparse sensor data

We present a computational framework to automatically discover high-order temporal social patterns from very noisy and sparse location data. We introduce the concept of social footprint and present a method to construct a codebook, enabling the transformation of raw sensor data into a collection of social pages. Each page captures social activities of a user over regular time period, and represented as a sequence of encoded footprints. Computable patterns are then dened as repeated structures found in these sequences. To do so, we appeal to modeling tools in document analysis and propose a Latent Social theme Dirichlet Allocation (LSDA) model a version of the Ngram topic model in [6] with extra modeling of personal context. This model can be viewed as a Bayesian clustering method, jointly discovering temporal collocation of footprints and exploiting statistical strength across social pages, to automatically discovery high-order patterns. Alternatively, it can be viewed as a dimension...

• Type: Conference
• Year: 2008
• Where: WWW
• Authors: Dinh Q. Phung, Brett Adams, Svetha Venkatesh
• Keywords: Internet Technology, Latent Social Theme, Social Pages, Temporal Social Patterns, WWW 2008