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  • Two-stage Unsupervised Approach for Combating Social Spammers

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    Foo459421-Accepted.pdf (398.3Kb)
    File version
    Accepted Manuscript (AM)
    Author(s)
    Koggalahewa, D
    Xu, Y
    Foo, E
    Griffith University Author(s)
    Foo, Ernest
    Year published
    2020
    Metadata
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    Abstract
    Spammers 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 ...
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    Spammers 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.
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    Conference Title
    2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020
    DOI
    https://doi.org/10.1109/SSCI47803.2020.9308315
    Copyright Statement
    © 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.
    Subject
    Information and computing sciences
    Publication URI
    http://hdl.handle.net/10072/401683
    Collection
    • Conference outputs

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