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Store Review Spammer Detection Based on Review Relationship

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Advances in Conceptual Modeling (ER 2013)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8697))

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Abstract

Reviews and comments play important role in online shopping. It can help people getting more information about stores and products. The potential customers tend to make decision according to it. However, driven by profit and fame, spammers post spurious reviews to mislead the customers by promoting or demoting target store. Previous studies mainly focused on the text features and ratings to identify fake reviews. However, these studies ignore the importance of relationship between store and reviewer. This paper first proposes sentiment analysis techniques to calculate the sentiment score of reviews. Then a relationship-based method has been proposed to identify the spammers. We also present an algorithm which can detect both single-mode spammers and multi-mode spammers. A subset of highly suspicious reviewers is selected for evaluation by human judges. Experimental results show that the proposed method can find out the review spammer efficiently.

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Author information

Authors and Affiliations

  1. State Key Laboratory of Software Engineering, Wuhan University, Luojiashan Road, 430072, Wuhan, China

    Qingxi Peng

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  1. Qingxi Peng
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Editor information

Editors and Affiliations

  1. Faculty of Business Administration, Memorial University of Newfoundland, A1B 3X5, St. John’s, NL, Canada

    Jeffrey Parsons

  2. Faculty of Education, Division of Information and Technology Studies, The University of Hong Kong, Hong Kong, China

    Dickson Chiu

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© 2014 Springer International Publishing Switzerland

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Peng, Q. (2014). Store Review Spammer Detection Based on Review Relationship. In: Parsons, J., Chiu, D. (eds) Advances in Conceptual Modeling. ER 2013. Lecture Notes in Computer Science, vol 8697. Springer, Cham. https://doi.org/10.1007/978-3-319-14139-8_30

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  • DOI: https://doi.org/10.1007/978-3-319-14139-8_30

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14138-1

  • Online ISBN: 978-3-319-14139-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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