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dc.contributor.authorZhao, Liangen
dc.contributor.authorChen, Fengen
dc.contributor.authorDai, Jingen
dc.contributor.authorHua, Tingen
dc.contributor.authorLu, Chang-Tienen
dc.contributor.authorRamakrishnan, Narenen
dc.description.abstractTwitter 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.en
dc.format.extent? - ? (12) page(s)en
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.subjectMultidisciplinary Sciencesen
dc.subjectScience & Technology - Other Topicsen
dc.titleUnsupervised Spatial Event Detection in Targeted Domains with Applications to Civil Unrest Modelingen
dc.typeArticle - Refereeden
dc.description.versionPublished (Publication status)en
dc.contributor.departmentComputer Scienceen
dc.title.serialPLOS ONEen
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/Computer Scienceen

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Creative Commons Attribution 4.0 International
License: Creative Commons Attribution 4.0 International