Dynamic feature selection for spam detection in twitter

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dc.contributor.authorKarakaşlı M.S.
dc.contributor.authorAydın, Muhammed Ali
dc.contributor.authorYarkan, Serkan
dc.contributor.authorBoyacı, Ali
dc.date.accessioned2020-11-21T15:54:18Z
dc.date.available2020-11-21T15:54:18Z
dc.date.issued2019en_US
dc.departmentİstanbul Ticaret Üniversitesien_US
dc.description1st International Telecommunications Conference, ITelCon 2017 -- 28 December 2017 through 29 December 2017 -- -- 215829en_US
dc.description.abstractSocial Networks continue to increase their popularity day by day. With the widespread availability of Internet access, interest of people in social networks has also increased significantly. The fact that, popularity of social media makes it tempting to use social media platforms for bad purposes. Malicious people are attempting to gain unfair profits by using fake accounts and various techniques. Among these initiatives, SPAM is one of the most frequently used methods. Today, SPAM attacks on social networks are increasing and many social network users are exposed to this and similar attacks. To identify SPAM users among billions of social network users, the examination of massive amounts of data requires a challenging large-scale data analysis. In this study, we group similar Twitter users and introduce a dynamic feature selection technique that use different features for each user groups instead of use static feature set and apply machine learning algorithms to classify spam users on Twitter. © 2019, Springer Nature Singapore Pte Ltd.en_US
dc.identifier.doi10.1007/978-981-13-0408-8_20en_US
dc.identifier.endpage250en_US
dc.identifier.issn1876-1100
dc.identifier.issn9.78981E+12
dc.identifier.scopus2-s2.0-85049965364en_US
dc.identifier.scopusqualityQ4en_US
dc.identifier.startpage239en_US
dc.identifier.urihttps://doi.org/10.1007/978-981-13-0408-8_20
dc.identifier.urihttps://hdl.handle.net/11467/3803
dc.identifier.volume504en_US
dc.identifier.wosWOS:000454345100020en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Verlagen_US
dc.relation.ispartofLecture Notes in Electrical Engineeringen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBig dataen_US
dc.subjectFeature selectionen_US
dc.subjectSocial mediaen_US
dc.subjectSpam detectionen_US
dc.titleDynamic feature selection for spam detection in twitteren_US
dc.typeConference Objecten_US

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