Discovering socio-spatio-temporal important locations of social media users

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dc.contributor.authorCelik M.
dc.contributor.authorDokuz A.S.
dc.date.accessioned2019-08-01T13:38:39Z
dc.date.available2019-08-01T13:38:39Z
dc.date.issued2017
dc.departmentNiğde ÖHÜ
dc.description.abstractSocio-spatio-temporal important locations (SSTILs) are places which are frequently visited by social media users in their social media history. Discovering SSTILs is important for several application domains, such as, recommender systems, advertisement applications, urban planning, etc. However, discovering SSTILs is challenging due to spatial, temporal, and social dimensions of the datasets, the lack of sufficient interest measures, and the need for developing computationally-efficient algorithms. In the literature, several methods are proposed to discover social important locations. However, these studies, usually, do not take into account temporal and social dimensions of the datasets and preferences of each user in a social group. In this study, we define SSTILs and SSTIL mining problem by taking into account spatial, temporal, and social dimensions of the social media datasets. We propose methods and interest measures to discover SSTILs efficiently based on both user and group preferences. The proposed algorithms were compared with a naïve alternative using real-life Twitter dataset. The results showed that the proposed algorithms outperform the naïve alternative. © 2017 Elsevier B.V.
dc.description.sponsorshipThis research was supported by the Research Fund of Erciyes University , Project Number FDK-2017-7233 . Mete Celik received the B.Sc. degree in control and computer engineering and the M.Sc. degree in electrical engineering from Erciyes University, Kayseri, Turkey, in 1999 and 2001, respectively, and the Ph.D. degree in computer science from the University of Minnesota, Minneapolis, USA, in 2008. He is currently a faculty member of the Department of Computer Engineering, Erciyes University, Turkey. His research interests include data analysis, spatial databases, spatial data mining, spatio-temporal data mining, and location-based services. He is a member of the IEEE and ACM. Ahmet Sakir Dokuz received the B.Sc. and the M.Sc degree in computer engineering and the M.Sc. degree from Erciyes University, Kayseri, Turkey, in 2010 and 2013, respectively. He is chasing his Ph.D. degree at Erciyes University, Turkey. He is currently a faculty member of the Department of Computer Engineering, Nigde Omer Halisdemir University, Turkey. His research interests include data analysis, spatial and spatio-temporal data mining, location-based services, and cloud computing.
dc.identifier.doi10.1016/j.jocs.2017.09.005
dc.identifier.endpage98
dc.identifier.issn1877-7503
dc.identifier.scopus2-s2.0-85029593352
dc.identifier.scopusqualityQ1
dc.identifier.startpage85
dc.identifier.urihttps://dx.doi.org/10.1016/j.jocs.2017.09.005
dc.identifier.urihttps://hdl.handle.net/11480/1781
dc.identifier.volume22
dc.identifier.wosWOS:000417667300008
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthor[0-Belirlenecek]
dc.language.isoen
dc.publisherElsevier B.V.
dc.relation.ispartofJournal of Computational Science
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectHistorical social media dataset
dc.subjectSocio-spatio-temporal important locations discovery
dc.subjectSpatial social media mining
dc.subjectSSTIL discovery
dc.subjectTwitter
dc.titleDiscovering socio-spatio-temporal important locations of social media users
dc.typeArticle

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