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Opinion Spam Detection in Online Reviews

Author

Listed:
  • Ajay Rastogi

    (Department of Computer Science, Jamia Millia Islamia, New Delhi, India)

  • Monica Mehrotra

    (Department of Computer Science, Jamia Millia Islamia, New Delhi, India)

Abstract

Online reviews are the most valuable sources of information about customer opinions and are considered the pillars on which the reputation of an organisation is built. From a customer’s perspective, review information is key to making a proper decision regarding an online purchase. Reviews are generally considered an unbiased opinion of an individual’s personal experience with a product, but the underlying truth about these reviews tells a different story. Spammers exploit these review platforms illegally because of incentives involved in writing fake reviews, thereby trying to gain an advantage over competitors resulting in an explosive growth of opinion spamming. The present study analyses and categorises the available literature on opinion spamming according to three detection targets: (1) opinion spam, (2) opinion spammers, and (3) collusive opinion spammer groups. The study further highlights and divides opinion spamming into three types based on textual and linguistic, behavioural, and relational features. Moreover, several state-of-the-art machine-learning techniques for opinion spam detection have also been discussed in the study. It concludes with a summary of the research articles on opinion spam detection and some interesting results to assist researchers for further exploration of the domain.

Suggested Citation

  • Ajay Rastogi & Monica Mehrotra, 2017. "Opinion Spam Detection in Online Reviews," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 16(04), pages 1-38, December.
    • Handle: RePEc:wsi:jikmxx:v:16:y:2017:i:04:n:s0219649217500368
    DOI: 10.1142/S0219649217500368
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    References listed on IDEAS

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    1. Michael Luca & Georgios Zervas, 2016. "Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud," Management Science, INFORMS, vol. 62(12), pages 3412-3427, December.
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    Cited by:

    1. Bingol, Harun & Alatas, Bilal, 2023. "Chaos enhanced intelligent optimization-based novel deception detection system," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    2. Sunil Saumya & Jyoti Prakash Singh, 2022. "Spam review detection using LSTM autoencoder: an unsupervised approach," Electronic Commerce Research, Springer, vol. 22(1), pages 113-133, March.

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