Foundations of statistical natural language processing
Foundations of statistical natural language processing
The Journal of Machine Learning Research
Sensing and using social context
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Topical N-Grams: Phrase and Topic Discovery, with an Application to Information Retrieval
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Unsupervised context detection using wireless signals
Pervasive and Mobile Computing
Extracting urban patterns from location-based social networks
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks
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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 defined 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 dimensionality reduction method where the reduced latent space can be interpreted as the hidden social 'theme' -- a more abstract perception of user's daily activities. Applying this framework to a real-world noisy dataset collected over 1.5 years, we show that many useful and interesting patterns can be computed. Interpretable social themes can also be deduced from the discovered patterns.