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

Abstract

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-temporalmore » 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.« less

Authors:
 [1];  [1];  [1];  [2];  [2];  [2];  [2]; ORCiD logo [3];  [4];  [5]
  1. Korea University
  2. Uncharted Software Inc
  3. ORNL
  4. Georgia Tech Research Institute (GTRI), Atlanta, GA
  5. Georgia Institute of Technology, Atlanta
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1426578
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
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

Citation Formats

Shin, Sungbok, Choi, Minsuk, Choi, Jinho, Langevin, Scott, Bethune, Christopher, Horne, Philippe, Kronenfeld, Nathan, Kannan, Ramakrishnan, Drake, Barry, and Park, Haesun. STExNMF: Spatio-Temporally Exclusive Topic Discovery for Anomalous Event Detection. United States: N. p., 2017. Web. doi:10.1109/ICDM.2017.53.
Shin, Sungbok, Choi, Minsuk, Choi, Jinho, Langevin, Scott, Bethune, Christopher, Horne, Philippe, Kronenfeld, Nathan, Kannan, Ramakrishnan, Drake, Barry, & Park, Haesun. STExNMF: Spatio-Temporally Exclusive Topic Discovery for Anomalous Event Detection. United States. https://doi.org/10.1109/ICDM.2017.53
Shin, Sungbok, Choi, Minsuk, Choi, Jinho, Langevin, Scott, Bethune, Christopher, Horne, Philippe, Kronenfeld, Nathan, Kannan, Ramakrishnan, Drake, Barry, and Park, Haesun. 2017. "STExNMF: Spatio-Temporally Exclusive Topic Discovery for Anomalous Event Detection". United States. https://doi.org/10.1109/ICDM.2017.53. https://www.osti.gov/servlets/purl/1426578.
@article{osti_1426578,
title = {STExNMF: Spatio-Temporally Exclusive Topic Discovery for Anomalous Event Detection},
author = {Shin, Sungbok and Choi, Minsuk and Choi, Jinho and Langevin, Scott and Bethune, Christopher and Horne, Philippe and Kronenfeld, Nathan and Kannan, Ramakrishnan and Drake, Barry and Park, Haesun},
abstractNote = {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.},
doi = {10.1109/ICDM.2017.53},
url = {https://www.osti.gov/biblio/1426578}, journal = {},
number = ,
volume = ,
place = {United States},
year = {2017},
month = {11}
}

Conference:
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