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State-of-art approaches for review spammer detection: a survey

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Abstract

E-commerce websites are now favourite for shopping comfortably at home without any burden of going to market. Their success depends upon the reviews written by the consumers who used particular products and subsequently shared their experiences with that product. The reviews also affects the buying decision of customer. Because of this reason the activity of fake reviews posting is increasing. The brand competitors of the product or the company itself may involve in posting fraud reviews to gain more profit. Such fraudulent reviews are spam review that badly affects the decision choice of the prospective consumer of the products. Many customers are misguided due to fake reviews. The person, who writes the fake reviews, is called the spammer. Identification of spammers is indirectly helpful in identifying whether the reviews are spam or not. The detection of review spammers is serious concern for the E-commerce business. To help researchers in this vibrant area, we present the state of art approaches for review spammer detection. This paper presents a comprehensive survey of the existing spammer detection approaches describing the features used for individual and group spammer detection, dataset summary with details of reviews, products and reviewers. The main aim of this paper is to provide a basic, comprehensive and comparative study of current research on detecting review spammer using machine learning techniques and give future directions. This paper also provides a concise summary of published research to help potential researchers in this area to innovate new techniques.

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Notes

  1. https://en.wikipedia.org/wiki/Spamming

  2. http://www.templetons.com/brad/spamreact.html

  3. https://en.wikipedia.org/wiki/Spamming

  4. https://www.medcalc.org/manual/logistic_regression.php

  5. http://snap.stanford.edu/data/web-Amazon-links.html

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Motilal Nehru National Institute of Technology, Allahabad, Uttar Pradesh, India, 211004

    Rupesh Kumar Dewang & Anil Kumar Singh

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  1. Rupesh Kumar Dewang
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  2. Anil Kumar Singh
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Correspondence to Rupesh Kumar Dewang.

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Dewang, R.K., Singh, A.K. State-of-art approaches for review spammer detection: a survey. J Intell Inf Syst 50, 231–264 (2018). https://doi.org/10.1007/s10844-017-0454-7

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  • DOI: https://doi.org/10.1007/s10844-017-0454-7

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