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-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.
Year
DOI
Venue
2017
10.1109/ICDM.2017.53
2017 IEEE International Conference on Data Mining (ICDM)
Keywords
Field
DocType
Topic modeling,social network analysis,matrix factorization,event detection,anomaly detection
Data mining,Data modeling,Social media,Time point,Matrix (mathematics),Computer science,Qualitative Evaluations,Non-negative matrix factorization,Topic model,Method of mean weighted residuals
Conference
ISSN
ISBN
Citations 
1550-4786
978-1-5386-2449-4
3
PageRank 
References 
Authors
0.37
16
11
Name
Order
Citations
PageRank
Dear Sungbok Shin140.73
minsuk choi292.51
Jinho Choi31642206.06
Scott Langevin4112.60
Christopher Bethune540.73
Philippe Horne640.73
Nathan Kronenfeld741.07
Ramakrishnan Kannan813318.57
Barry L. Drake910011.59
Haesun Park103546232.42
Jaegul Choo1155646.81