Detecting Group Review Spammers in Social Media

Teimoori, Z.; Salehi, M.; Ranjbar, V.; Shehnepoor, Saeed R.; Najari, Sh.

doi:10.22044/jadm.2022.10981.2245

Document Type : Original/Review Paper

Authors

¹ Faculty of New Sciences and Technology, University of Tehran, Tehran, Iran.

² School of Computer Science, Institute for Research in Fundamental Science (IPM), Tehran, Iran.

³ Department of Computer Engineering, Yazd University, Yazd, Iran.

⁴ Department of Electrical, Electronic, and Computer Engineering, University of Western Australia, Perth, Australia.

https://doi.org/10.22044/jadm.2022.10981.2245

Abstract

Nowadays, some e-advice websites and social media like e-commerce businesses, provide not only their goods but a new way that their customers can give their opinions about products. Meanwhile, there are some review spammers who try to promote or demote some specific products by writing fraud reviews. There have been several types of researches and studies toward detecting these review spammers, but most studies are based on individual review spammers and few of them studied group review spammers, nevertheless it should be mentioned that review spammers can increase their effects by cooperating and working together. More words, there have been many features introduced in order to detect review spammers and it is better to use the efficient ones. In this paper we propose a novel framework, named Network Based Group Review Spammers which tries to identify and classify group review spammers with the usage of the heterogeneous information network. In addition to eight basic features for detecting group review spammers, three efficient new features from previous studies were modified and added in order to improve detecting group review spammers. Then with the definition of Meta-path, features are ranked. Results showed that by using the importance of features and adding three new features in the suggested framework, group review spammers detection is improved on Amazon dataset.

Keywords

20.1001.1.23225211.2022.10.2.11.1

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Detecting Group Review Spammers in Social Media

References

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Volume 10, Issue 2April 2022Pages 269-283

Volume 10, Issue 2
April 2022
Pages 269-283