Discovering socially similar users in social media datasets based on their socially important locations

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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.issued2018
dc.departmentNiğde ÖHÜ
dc.description.abstractSocially similar social media users can be defined as users whose frequently visited locations in their social media histories are similar. Discovering socially similar social media users is important for several applications, such as, community detection, friendship analysis, location recommendation, urban planning, and anomaly user and behavior detection. Discovering socially similar users is challenging due to dataset size and dimensions, spam behaviors of social media users, spatial and temporal aspects of social media datasets, and location sparseness in social media datasets. In the literature, several studies are conducted to discover similar social media users out of social media datasets using spatial and temporal information. However, most of these studies rely on trajectory pattern mining methods or take into account semantic information of social media datasets. Limited number of studies focus on discovering similar users based on their social media location histories. In this study, to discover socially similar users, frequently visited or socially important locations of social media users are taken into account instead of all locations that users visited. A new interest measure, which is based on Levenshtein distance, was proposed to quantify user similarity based on their socially important locations and two algorithms were developed using the proposed method and interest measure. The algorithms were experimentally evaluated on a real-life Twitter dataset. The results show that the proposed algorithms could successfully discover similar social media users based on their socially important locations. © 2018 Elsevier Ltd
dc.description.sponsorshipThis research was supported by the Research Fund of Erciyes University , Project Number: FDK-2017-7233 .
dc.identifier.doi10.1016/j.ipm.2018.08.004
dc.identifier.endpage1168
dc.identifier.issn0306-4573
dc.identifier.issue6
dc.identifier.scopus2-s2.0-85051644622
dc.identifier.scopusqualityQ1
dc.identifier.startpage1154
dc.identifier.urihttps://dx.doi.org/10.1016/j.ipm.2018.08.004
dc.identifier.urihttps://hdl.handle.net/11480/1588
dc.identifier.volume54
dc.identifier.wosWOS:000445713800018
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthor[0-Belirlenecek]
dc.language.isoen
dc.publisherElsevier Ltd
dc.relation.ispartofInformation Processing and Management
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectMining user similarity
dc.subjectSocially important locations
dc.subjectSocially similar users
dc.subjectSpatial social media mining
dc.subjectTwitter
dc.titleDiscovering socially similar users in social media datasets based on their socially important locations
dc.typeArticle

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