A Framework for Discovering Evolving Domain Related Spatio-Temporal Patterns in Twitter

Simple item page
dc.contributor.authorShi, Yanen
dc.contributor.authorDeng, Minen
dc.contributor.authorYang, Xuexien
dc.contributor.authorLiu, Qiliangen
dc.contributor.authorZhao, Liangen
dc.contributor.authorLu, Chang-Tienen
dc.contributor.departmentVirginia Agricultural Experiment Stationen
dc.date.accessioned2017-09-20T18:26:21Zen
dc.date.available2017-09-20T18:26:21Zen
dc.date.issued2016-10-18en
dc.date.updated2017-09-20T18:26:21Zen
dc.description.abstractIn massive Twitter datasets, tweets deriving from different domains, e.g., civil unrest, can be extracted to constitute spatio-temporal Twitter events for spatio-temporal distribution pattern detection. Existing algorithms generally employ scan statistics to detect spatio-temporal hotspots from Twitter events and do not consider the spatio-temporal evolving process of Twitter events. In this paper, a framework is proposed to discover evolving domain related spatio-temporal patterns from Twitter data. Given a target domain, a dynamic query expansion is employed to extract related tweets to form spatio-temporal Twitter events. The new spatial clustering approach proposed here is based on the use of multi-level constrained Delaunay triangulation to capture the spatial distribution patterns of Twitter events. An additional spatio-temporal clustering process is then performed to reveal spatio-temporal clusters and outliers that are evolving into spatial distribution patterns. Extensive experiments on Twitter datasets related to an outbreak of civil unrest in Mexico demonstrate the effectiveness and practicability of the new method. The proposed method will be helpful to accurately predict the spatio-temporal evolution process of Twitter events, which belongs to a deeper geographical analysis of spatio-temporal Big Data.en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationShi, Y.; Deng, M.; Yang, X.; Liu, Q.; Zhao, L.; Lu, C.-T. A Framework for Discovering Evolving Domain Related Spatio-Temporal Patterns in Twitter. ISPRS Int. J. Geo-Inf. 2016, 5, 193.en
dc.identifier.doihttps://doi.org/10.3390/ijgi5100193en
dc.identifier.urihttp://hdl.handle.net/10919/79280en
dc.language.isoenen
dc.publisherMDPIen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectEvolving spatio-temporal patternsen
dc.subjecttarget domainsen
dc.subjectspatio-temporal Twitter eventsen
dc.subjectspatial clusteringen
dc.subjectspatio-temporal clusteringen
dc.titleA Framework for Discovering Evolving Domain Related Spatio-Temporal Patterns in Twitteren
dc.title.serialISPRS International Journal of Geo-Informationen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
ijgi-05-00193.pdf
Size:
11.77 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
Name:
license.txt
Size:
1.5 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Journal Articles, Multidisciplinary Digital Publishing Institute (MDPI)
Scholarly Works, Virginia Agricultural Experiment Station