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dc.contributor.authorG. Almatar, Muhammaden
dc.contributor.authorAlazmi, Huda S.en
dc.contributor.authorLi, Liuqingen
dc.contributor.authorFox, Edward A.en
dc.date.accessioned2020-11-30T12:51:14Zen
dc.date.available2020-11-30T12:51:14Zen
dc.date.issued2020-11-25en
dc.identifier.citationG. Almatar, M.; Alazmi, H.S.; Li, L.; Fox, E.A. Applying GIS and Text Mining Methods to Twitter Data to Explore the Spatiotemporal Patterns of Topics of Interest in Kuwait. ISPRS Int. J. Geo-Inf. 2020, 9, 702.en
dc.identifier.urihttp://hdl.handle.net/10919/100965en
dc.description.abstractResearchers have developed various approaches for exploring the spatial information, temporal patterns, and Twitter content in topics of interest in order to generate a better understanding of human behavior; however, few investigations have integrated these three dimensions simultaneously. This study analyzes the content of tweets in order to conduct a spatiotemporal exploration of the main topics of interest in Kuwait in order to provide a deeper understanding of the topics people think about, when they think about them, and where they tweet about them. To this end, we collect, process, and analyze tweets from nearly 120 areas in Kuwait over a 10-month period. The study’s results indicate that religion, emotions, education, and public policy are the most popular topics of interest in Kuwait. Regarding the spatiotemporal analysis, people post more tweets regarding religion on Fridays, a holy day for Muslims in Kuwait. Moreover, people are more likely to tweet about policy and education on weekdays rather than weekends. In contrast, people tweet about emotional expressions more often on weekends. From the spatial perspectives, spatial clustering in topics occurs across the days of the week. The findings are applicable to further topic analysis and similar research in other countries.en
dc.format.mimetypeapplication/pdfen
dc.language.isoenen
dc.publisherMDPIen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectGISen
dc.subjecttext miningen
dc.subjectspatiotemporal patternen
dc.subjecttopic of interesten
dc.subjectTwitteren
dc.titleApplying GIS and Text Mining Methods to Twitter Data to Explore the Spatiotemporal Patterns of Topics of Interest in Kuwaiten
dc.typeArticle - Refereeden
dc.date.updated2020-11-26T14:09:48Zen
dc.description.versionPublished versionen
dc.contributor.departmentComputer Scienceen
dc.title.serialInternational Journal of Geo-Informationen
dc.identifier.doihttps://doi.org/10.3390/ijgi9120702en
dc.type.dcmitypeTexten
dc.type.dcmitypeStillImageen


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