NDLI: Computable social patterns from sparse sensor data

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Computable social patterns from sparse sensor data

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Computable social patterns from sparse sensor data

Content Provider	ACM Digital Library
Author	Phung, Dinh Venkatesh, Svetha Adams, Brett
Abstract	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.
Starting Page	69
Ending Page	72
Page Count	4
File Format	PDF
ISBN	9781605581606
DOI	10.1145/1367798.1367810
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2008-04-22
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Social pattern Latent dirichlet allocation Social footprints
Content Type	Text
Resource Type	Article

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