STExNMF: Spatio-Temporally Exclusive Topic Discovery for Anomalous Event Detection

Shin, Sungbok; Choi, Minsuk; Choi, Jinho; Langevin, Scott; Bethune, Christopher; Horne, Philippe; Kronenfeld, Nathan; Kannan, Ramakrishnan {ramki}; Drake, Barry; Park, Haesun

doi:10.1109/ICDM.2017.53

Title: STExNMF: Spatio-Temporally Exclusive Topic Discovery for Anomalous Event Detection

Conference · Wed Nov 01 00:00:00 EDT 2017

DOI:https://doi.org/10.1109/ICDM.2017.53· OSTI ID:1426578

Shin, Sungbok ^[1]; Choi, Minsuk ^[1]; Choi, Jinho ^[1]; Langevin, Scott ^[2]; Bethune, Christopher ^[2]; Horne, Philippe ^[2]; Kronenfeld, Nathan ^[2];

^[3]; Drake, Barry ^[4]; Park, Haesun ^[5]

Korea University
Uncharted Software Inc
ORNL
Georgia Tech Research Institute (GTRI), Atlanta, GA
Georgia Institute of Technology, Atlanta

Understanding newly emerging events or topics associated with a particular region of a given day can provide deep insight on the critical events occurring in highly evolving metropolitan cities. We propose herein a novel topic modeling approach on text documents with spatio-temporal information (e.g., when and where a document was published) such as location-based social media data to discover prevalent topics or newly emerging events with respect to an area and a time point. We consider a map view composed of regular grids or tiles with each showing topic keywords from documents of the corresponding region. To this end, we present a tilebased spatio-temporally exclusive topic modeling approach called STExNMF, based on a novel nonnegative matrix factorization (NMF) technique. STExNMF mainly works based on the two following stages: (1) first running a standard NMF of each tile to obtain general topics of the tile and (2) running a spatiotemporally exclusive NMF on a weighted residual matrix. These topics likely reveal information on newly emerging events or topics of interest within a region. We demonstrate the advantages of our approach using the geo-tagged Twitter data of New York City. We also provide quantitative comparisons in terms of the topic quality, spatio-temporal exclusiveness, topic variation, and qualitative evaluations of our method using several usage scenarios. In addition, we present a fast topic modeling technique of our model by leveraging parallel computing.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE

DOE Contract Number:: AC05-00OR22725

OSTI ID:: 1426578

Resource Relation:: Conference: IEEE International Conference on Data Mining (ICDM) - New Orleans, Louisiana, United States of America - 11/18/2017 3:00:00 PM-11/21/2017 3:00:00 PM

Country of Publication:: United States

Language:: English

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