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dc.contributor.authorYavuz, D.D.
dc.contributor.authorAbul, Osman
dc.date.accessioned2019-07-10T14:42:44Z
dc.date.available2019-07-10T14:42:44Z
dc.date.issued2016
dc.identifier.citationYavuz, D. D., & Abul, O. (2016, August). Implicit Location Sharing Detection in Social Media Turkish Text Messaging. In International Workshop on Machine Learning, Optimization, and Big Data (pp. 341-352). Springer, Cham.en_US
dc.identifier.issn3029743
dc.identifier.urihttps://link.springer.com/chapter/10.1007%2F978-3-319-51469-7_29
dc.identifier.urihttp://hdl.handle.net/20.500.11851/1988
dc.description2nd International Workshop on Machine Learning, Optimization and Big Data (2016 : Volterra; Italy)
dc.description.abstractSocial media have become a significant venue for information sharing of live updates. Users of social media are producing and sharing large amount of personal data as a part of the live updates. A significant share of this data contains location information that can be used by other people for many purposes. Some of the social media users deliberately share their own location information with other users. However, a large number of users blindly or implicitly share their own location without noticing it and its possible consequences. Implicit location sharing is investigated in the current paper. We perform a large scale study on implicit location sharing detection for one of the most popular social media platform, namely Twitter. After a careful study, we prepared a training data set of Turkish tweets and manually labelled them. Using machine learning techniques we induced classifiers that are able to classify whether a given tweet contains implicit location sharing or not. The classifiers are shown to be very accurate and efficient as well. Moreover, the best classifier is employed in a browser add-on tool which warns the user whenever an implicit location sharing is predicted from just to be released tweet. The paper provides the followed methodology and the technical analysis as well. Furthermore, it discusses how these techniques can be extended to different social network services and also to different languages. © Springer International Publishing AG 2016.en_US
dc.language.isoengen_US
dc.publisherSpringer Verlagen_US
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectDisastersen_US
dc.subjectSocial networking (online)en_US
dc.subjectevent detectionen_US
dc.titleImplicit location sharing detection in social media turkish text messagingen_US
dc.typeconferenceObjecten_US
dc.relation.journalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_US
dc.contributor.departmentTOBB ETU, Faculty of Engineering, Department of Computer Engineeringen_US
dc.contributor.departmentTOBB ETÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.identifier.volume10122 LNCS
dc.identifier.startpage341
dc.identifier.endpage352
dc.contributor.orcidhttps://orcid.org/0000-0002-9284-6112
dc.identifier.scopus2-s2.0-85009516180
dc.contributor.tobbetuauthorAbul, Osman
dc.contributor.YOKid31072
dc.identifier.doi10.1007/978-3-319-51469-7_29
dc.contributor.ScopusAuthorID6602597612
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıtr_TR


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