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Article: A burst-based unsupervised method for detecting review spammer groups

TitleA burst-based unsupervised method for detecting review spammer groups
Authors
KeywordsOpinion spam
Spammer groups
Fraud detection
Burst detection
Spam indicators
Issue Date2020
PublisherElsevier Inc. The Journal's web site is located at http://www.elsevier.com/locate/ins
Citation
Information Sciences, 2020, v. 536, p. 454-469 How to Cite?
AbstractWith the development of e-commerce, online shopping has become a part of people's life. As customers often refer to online product reviews for shopping, sellers often collude with review spammers in writing fake reviews to promote or demote target products. In particular, spammers working in groups are more harmful than individual attacks. To detect such spammer groups, previous researchers proposed some frequent item mining based algorithms and graph-based algorithms. In this paper, we propose a method called GSDB (Group Spam Detection algorithm based on review Burst). Our algorithm first locates target products attacked by spammers by detecting the abnormality of product rating distribution. As group spammers usually post many fake reviews within a short period, we design a burst-based algorithm that discovers candidate spammer groups in reviewbursts using the Kernel Density Estimation algorithm. As some innocent reviewers may coincidently review during the burst period, we formulate a variety of individual spam indicators to measure the spamicity of the reviewers to isolate the candidate spammer groups. Finally, we design a series of group spam indicators to measure and classify the spamicity of spammer groups. Experimental results show that our proposed GSDB algorithm outperforms state-of-the-art algorithms.
Persistent Identifierhttp://hdl.handle.net/10722/286376
ISSN
2019 Impact Factor: 5.91
2015 SCImago Journal Rankings: 2.513
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorJi, S-
dc.contributor.authorZhang, Q-
dc.contributor.authorLi, J-
dc.contributor.authorChiu, DKW-
dc.contributor.authorXu, S-
dc.contributor.authorYi, L-
dc.contributor.authorGong, M-
dc.date.accessioned2020-08-31T07:02:59Z-
dc.date.available2020-08-31T07:02:59Z-
dc.date.issued2020-
dc.identifier.citationInformation Sciences, 2020, v. 536, p. 454-469-
dc.identifier.issn0020-0255-
dc.identifier.urihttp://hdl.handle.net/10722/286376-
dc.description.abstractWith the development of e-commerce, online shopping has become a part of people's life. As customers often refer to online product reviews for shopping, sellers often collude with review spammers in writing fake reviews to promote or demote target products. In particular, spammers working in groups are more harmful than individual attacks. To detect such spammer groups, previous researchers proposed some frequent item mining based algorithms and graph-based algorithms. In this paper, we propose a method called GSDB (Group Spam Detection algorithm based on review Burst). Our algorithm first locates target products attacked by spammers by detecting the abnormality of product rating distribution. As group spammers usually post many fake reviews within a short period, we design a burst-based algorithm that discovers candidate spammer groups in reviewbursts using the Kernel Density Estimation algorithm. As some innocent reviewers may coincidently review during the burst period, we formulate a variety of individual spam indicators to measure the spamicity of the reviewers to isolate the candidate spammer groups. Finally, we design a series of group spam indicators to measure and classify the spamicity of spammer groups. Experimental results show that our proposed GSDB algorithm outperforms state-of-the-art algorithms.-
dc.languageeng-
dc.publisherElsevier Inc. The Journal's web site is located at http://www.elsevier.com/locate/ins-
dc.relation.ispartofInformation Sciences-
dc.subjectOpinion spam-
dc.subjectSpammer groups-
dc.subjectFraud detection-
dc.subjectBurst detection-
dc.subjectSpam indicators-
dc.titleA burst-based unsupervised method for detecting review spammer groups-
dc.typeArticle-
dc.identifier.emailChiu, DKW: dchiu88@hku.hk-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.ins.2020.05.084-
dc.identifier.scopuseid_2-s2.0-85086563351-
dc.identifier.hkuros313604-
dc.identifier.volume536-
dc.identifier.spage454-
dc.identifier.epage469-
dc.identifier.isiWOS:000556340600026-
dc.publisher.placeUnited States-
dc.identifier.issnl0020-0255-

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