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dc.contributor.authorKoggalahewa, D
dc.contributor.authorXu, Y
dc.contributor.authorFoo, E
dc.date.accessioned2021-02-04T06:28:09Z
dc.date.available2021-02-04T06:28:09Z
dc.date.issued2020
dc.identifier.isbn9781728125473
dc.identifier.doi10.1109/SSCI47803.2020.9308315
dc.identifier.urihttp://hdl.handle.net/10072/401683
dc.description.abstractSpammers use Online Social Networks (OSNs) as a popular platform for spreading malicious content and links. The nature of OSNs allows the spammers to bypass the combating techniques by changing their behaviours. Classification based approaches are the most common technique for spam detection. 'Data labelling' 'spam drift' 'imbalanced datasets' and 'data fabrication' are the most common limitations of classification techniques that hinder the accuracy of spam detection. The paper presents a two-stage fully unsupervised approach using a user's peer acceptance within OSN to distinguish spammers from genuine users. User's common shared interest over multiple topics and the mentioning behaviour are used to derive the peer acceptance. The contribution of the paper is a pure unsupervised method to detect spammers based on users' peer acceptance without labelled datasets. Our unsupervised approach is able to achieve 95.9% accuracy without the need for labelling.
dc.description.peerreviewedYes
dc.publisherIEEE
dc.relation.ispartofconferencename2020 IEEE Symposium Series on Computational Intelligence (SSCI)
dc.relation.ispartofconferencetitle2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020
dc.relation.ispartofdatefrom2020-12-01
dc.relation.ispartofdateto2020-12-04
dc.relation.ispartofpagefrom71
dc.relation.ispartofpageto78
dc.subject.fieldofresearchInformation and computing sciences
dc.subject.fieldofresearchcode46
dc.titleTwo-stage Unsupervised Approach for Combating Social Spammers
dc.typeConference output
dc.type.descriptionE1 - Conferences
dcterms.bibliographicCitationKoggalahewa, D; Xu, Y; Foo, E, Two-stage Unsupervised Approach for Combating Social Spammers, 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020, 2020, pp. 71-78
dc.date.updated2021-02-04T05:29:08Z
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
gro.hasfulltextFull Text
gro.griffith.authorFoo, Ernest


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