Computable social patterns from sparse sensor data

Phung, Dinh, Adams, Brett and Venkatesh, Svetha 2008, Computable social patterns from sparse sensor data, in LocWeb 2008 : Proceedings of the 1st International Workshop on Location and the web, ACM, New York, N. Y., pp. 69-72.

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Title Computable social patterns from sparse sensor data
Author(s) Phung, DinhORCID iD for Phung, Dinh
Adams, Brett
Venkatesh, SvethaORCID iD for Venkatesh, Svetha
Conference name International Workshop on Location and the Web (1st : 2008 : Beijing, China)
Conference location Beijing, China
Conference dates 22 Apr. 2008
Title of proceedings LocWeb 2008 : Proceedings of the 1st International Workshop on Location and the web
Editor(s) [Unknown]
Publication date 2008
Conference series International Workshop on Location and the Web
Start page 69
End page 72
Total pages 4
Publisher ACM
Place of publication New York, N. Y.
Keyword(s) latent Dirichlet allocation
social footprints
social pattern
Summary 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.
ISBN 9781605581606
Language eng
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2008, ACM
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