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dc.contributor.authorHo, K.
dc.contributor.authorLiesaputra, Veronica
dc.contributor.authorYongchareon, Dr. Sira
dc.contributor.authorMohaghegh, Dr Mahsa
dc.contributor.editorPanetto H. et al.
dc.date.accessioned2018-06-15T02:46:25Z
dc.date.available2018-06-15T02:46:25Z
dc.date.issued2017-10-20
dc.identifier.isbn9783319694610
dc.identifier.isbn9783319694627
dc.identifier.issn0302-9743
dc.identifier.urihttps://hdl.handle.net/10652/4286
dc.description.abstractDespite several benefits to modern communities and businesses, Twitter has attracted many spammers overwhelming legitimate users with unwanted and disruptive advertising and fake information. Detecting spammers is always challenging because there is a huge volume of data that needs to be analyzed while at the mean time spammers continue learning and changing their ways to avoid being detected by anti-spammer systems. Several spam classification systems are proposed using various features extracted from the content and user’s information from their Tweets. Nevertheless, no comprehensive study has been done to compare and evaluate the effectiveness and efficiency of these systems. It is not known what the best anti-spammer system is and why. This paper proposes an evaluation framework that allows researchers, developers, and practitioners to access existing user-based and content-based features, implement their own features, and evaluate the performance of their systems against other systems. Our framework helps identify the most effective and efficient spammer detection features, evaluate the impact of using different numbers of recent tweets, and therefore obtaining a faster and more accurate classifier model.en_NZ
dc.language.isoenen_NZ
dc.publisherSpringer Verlagen_NZ
dc.rights© Springer International Publishing AG 2017en_NZ
dc.subjectTwitteren_NZ
dc.subjectspam detectionen_NZ
dc.subjectevaluation workbenchen_NZ
dc.subjectfeature selectionen_NZ
dc.subjectmachine learningen_NZ
dc.titleA framework for evaluating anti spammer systems for Twitteren_NZ
dc.typeConference Contribution - Paper in Published Proceedingsen_NZ
dc.date.updated2018-01-09T13:30:09Z
dc.identifier.doihttps://doi.org/10.1007/978-3-319-69462-7_41en_NZ
dc.subject.marsden080303 Computer System Securityen_NZ
dc.subject.marsden080109 Pattern Recognition and Data Miningen_NZ
dc.identifier.bibliographicCitationHo, K., Liesaputra, V., Yongchareon, S., & Mohaghegh, M. (2017, October). A Framework for Evaluating Anti Spammer Systems for Twitter. Panetto H. et al. (Ed.), 25th International Conference on Cooperative Information Systems, On the Move to Meaningful Internet Systems. OTM 2017 Conferences. OTM 2017. (pp.online). 10573. 10.1007/978-3-319-69462-7_41.en_NZ
unitec.publication.spageonlineen_NZ
unitec.publication.volume10573en_NZ
unitec.conference.title25th International Conference on Cooperative Information Systems, On the Move to Meaningful Internet Systems. OTM 2017 Conferences. OTM 2017.en_NZ
unitec.conference.sdate2017-10
unitec.conference.edate2017-10
unitec.peerreviewedyesen_NZ
dc.contributor.affiliationUnitec Institute of Technologyen_NZ
unitec.identifier.roms60378en_NZ
unitec.publication.placeGermanyen_NZ
unitec.institution.studyareaComputing


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