Unsupervised spatial event detection in targeted domains with applications to civil unrest modeling.
Twitter has become a popular data source as a surrogate for monitoring and detecting events. Targeted domains such as crime, election, and social unrest require the creation of algorithms capable of detecting events pertinent to these domains. Due to the unstructured language, short-length messages,...
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2014-01-01
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doaj-be4eaef3e5824bd784aaf001258a26dc2020-11-25T00:46:33ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-01910e11020610.1371/journal.pone.0110206Unsupervised spatial event detection in targeted domains with applications to civil unrest modeling.Liang ZhaoFeng ChenJing DaiTing HuaChang-Tien LuNaren RamakrishnanTwitter has become a popular data source as a surrogate for monitoring and detecting events. Targeted domains such as crime, election, and social unrest require the creation of algorithms capable of detecting events pertinent to these domains. Due to the unstructured language, short-length messages, dynamics, and heterogeneity typical of Twitter data streams, it is technically difficult and labor-intensive to develop and maintain supervised learning systems. We present a novel unsupervised approach for detecting spatial events in targeted domains and illustrate this approach using one specific domain, viz. civil unrest modeling. Given a targeted domain, we propose a dynamic query expansion algorithm to iteratively expand domain-related terms, and generate a tweet homogeneous graph. An anomaly identification method is utilized to detect spatial events over this graph by jointly maximizing local modularity and spatial scan statistics. Extensive experiments conducted in 10 Latin American countries demonstrate the effectiveness of the proposed approach.http://europepmc.org/articles/PMC4211687?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Liang Zhao Feng Chen Jing Dai Ting Hua Chang-Tien Lu Naren Ramakrishnan |
spellingShingle |
Liang Zhao Feng Chen Jing Dai Ting Hua Chang-Tien Lu Naren Ramakrishnan Unsupervised spatial event detection in targeted domains with applications to civil unrest modeling. PLoS ONE |
author_facet |
Liang Zhao Feng Chen Jing Dai Ting Hua Chang-Tien Lu Naren Ramakrishnan |
author_sort |
Liang Zhao |
title |
Unsupervised spatial event detection in targeted domains with applications to civil unrest modeling. |
title_short |
Unsupervised spatial event detection in targeted domains with applications to civil unrest modeling. |
title_full |
Unsupervised spatial event detection in targeted domains with applications to civil unrest modeling. |
title_fullStr |
Unsupervised spatial event detection in targeted domains with applications to civil unrest modeling. |
title_full_unstemmed |
Unsupervised spatial event detection in targeted domains with applications to civil unrest modeling. |
title_sort |
unsupervised spatial event detection in targeted domains with applications to civil unrest modeling. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2014-01-01 |
description |
Twitter has become a popular data source as a surrogate for monitoring and detecting events. Targeted domains such as crime, election, and social unrest require the creation of algorithms capable of detecting events pertinent to these domains. Due to the unstructured language, short-length messages, dynamics, and heterogeneity typical of Twitter data streams, it is technically difficult and labor-intensive to develop and maintain supervised learning systems. We present a novel unsupervised approach for detecting spatial events in targeted domains and illustrate this approach using one specific domain, viz. civil unrest modeling. Given a targeted domain, we propose a dynamic query expansion algorithm to iteratively expand domain-related terms, and generate a tweet homogeneous graph. An anomaly identification method is utilized to detect spatial events over this graph by jointly maximizing local modularity and spatial scan statistics. Extensive experiments conducted in 10 Latin American countries demonstrate the effectiveness of the proposed approach. |
url |
http://europepmc.org/articles/PMC4211687?pdf=render |
work_keys_str_mv |
AT liangzhao unsupervisedspatialeventdetectionintargeteddomainswithapplicationstocivilunrestmodeling AT fengchen unsupervisedspatialeventdetectionintargeteddomainswithapplicationstocivilunrestmodeling AT jingdai unsupervisedspatialeventdetectionintargeteddomainswithapplicationstocivilunrestmodeling AT tinghua unsupervisedspatialeventdetectionintargeteddomainswithapplicationstocivilunrestmodeling AT changtienlu unsupervisedspatialeventdetectionintargeteddomainswithapplicationstocivilunrestmodeling AT narenramakrishnan unsupervisedspatialeventdetectionintargeteddomainswithapplicationstocivilunrestmodeling |
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1725264541487464448 |