Year-end Sale % OFF 
Abstract
Online Social Networks (OSNs) are a popular platform for communication and collaboration. Spammers are highly active in OSNs. Uncovering spammers has become one of the most challenging problems in OSNs. Classification-based supervised approaches are the most commonly used method for detecting spammers. Classification-based systems suffer from limitations of “data labelling”, “spam drift”, “imbalanced datasets” and “data fabrication”. These limitations effect the accuracy of a classifier’s detection. An unsupervised approach does not require labelled datasets. We aim to address the limitation of data labelling and spam drifting through an unsupervised approach.We present a pure unsupervised approach for spammer detection based on the peer acceptance of a user in a social network to distinguish spammers from genuine users. The peer acceptance of a user to another user is calculated based on common shared interests over multiple shared topics between the two users. The main contribution of this paper is the introduction of a pure unsupervised spammer detection approach based on users’ peer acceptance. Our approach does not require labelled training datasets. While it does not better the accuracy of supervised classification-based approaches, our approach has become a successful alternative for traditional classifiers for spam detection by achieving an accuracy of 96.9%.
Highlights
Online Social Networks (OSNs) provide a wide range of communication and collaboration opportunities
Integrated with clustering, the spam detection based on peer acceptance can increase the detection accuracy by 6 percent compared to the detection accuracy achieved by using the peer acceptance only, according to our experimental results to be reported below
As discussed in Section “Mutual peer acceptance”, in order to address the high false negative problem caused by high content similarity in spam posts, the mutual peer acceptance distance is used to further differentiate spammers from genuine users
Summary
Online Social Networks (OSNs) provide a wide range of communication and collaboration opportunities. They provide a platform to build up new social relationships. OSN spammers are a set of users who manipulate the social media platform through their activities. Platform manipulation activities are any form of behaviours which intend to negatively impact the experience of Twitter users. Some of these behaviours are: “Posting duplicate or very similar content across multiple accounts”, “Posting multiple updates in an attempt to manipulate or undermine Twitter trends”, “Posting multiple, duplicate updates on one Koggalahewa et al Journal of Big Data (2022) 9:7 accounts” etc.. Support Vector Machines (SVM), Random forest and Artificial Neural Networks (ANNs) are among the most popular classification-based methods used for spam detection [2]. Classifiers developed for spam detection suffered with open issues of data labelling and spam drifting [5,6,7]
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
