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Creating and detecting fake reviews of online products

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  • Salminen, Joni
  • Kandpal, Chandrashekhar
  • Kamel, Ahmed Mohamed
  • Jung, Soon-gyo
  • Jansen, Bernard J.

Abstract

Customers increasingly rely on reviews for product information. However, the usefulness of online reviews is impeded by fake reviews that give an untruthful picture of product quality. Therefore, detection of fake reviews is needed. Unfortunately, so far, automatic detection has only had partial success in this challenging task. In this research, we address the creation and detection of fake reviews. First, we experiment with two language models, ULMFiT and GPT-2, to generate fake product reviews based on an Amazon e-commerce dataset. Using the better model, GPT-2, we create a dataset for a classification task of fake review detection. We show that a machine classifier can accomplish this goal near-perfectly, whereas human raters exhibit significantly lower accuracy and agreement than the tested algorithms. The model was also effective on detected human generated fake reviews. The results imply that, while fake review detection is challenging for humans, “machines can fight machines†in the task of detecting fake reviews. Our findings have implications for consumer protection, defense of firms from unfair competition, and responsibility of review platforms.

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  • Salminen, Joni & Kandpal, Chandrashekhar & Kamel, Ahmed Mohamed & Jung, Soon-gyo & Jansen, Bernard J., 2022. "Creating and detecting fake reviews of online products," Journal of Retailing and Consumer Services, Elsevier, vol. 64(C).
    • Handle: RePEc:eee:joreco:v:64:y:2022:i:c:s0969698921003374
    DOI: 10.1016/j.jretconser.2021.102771
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    References listed on IDEAS

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    2. Shawn Berry, 2024. "Fake Google restaurant reviews and the implications for consumers and restaurants," Papers 2401.11345, arXiv.org.
    3. Hui Zhao & Xiaoyuan Wang & Debing Ni & Kevin W. Li, 2023. "The Quality-Signaling Role of Manipulated Consumer Reviews," Group Decision and Negotiation, Springer, vol. 32(3), pages 503-536, June.
    4. Ben Jabeur, Sami & Ballouk, Hossein & Ben Arfi, Wissal & Sahut, Jean-Michel, 2023. "Artificial intelligence applications in fake review detection: Bibliometric analysis and future avenues for research," Journal of Business Research, Elsevier, vol. 158(C).
    5. Costa Filho, Murilo & Nogueira Rafael, Diego & Salmonson Guimarães Barros, Lucia & Mesquita, Eduardo, 2023. "Mind the fake reviews! Protecting consumers from deception through persuasion knowledge acquisition," Journal of Business Research, Elsevier, vol. 156(C).
    6. Wang, Zheng & Wang, Lun & Ji, Ying & Zuo, Lulu & Qu, Shaojian, 2022. "A novel data-driven weighted sentiment analysis based on information entropy for perceived satisfaction," Journal of Retailing and Consumer Services, Elsevier, vol. 68(C).
    7. Mustak, Mekhail & Salminen, Joni & Mäntymäki, Matti & Rahman, Arafat & Dwivedi, Yogesh K., 2023. "Deepfakes: Deceptions, mitigations, and opportunities," Journal of Business Research, Elsevier, vol. 154(C).
    8. Perez, Dikla & Stockheim, Inbal & Baratz, Guy, 2022. "Complimentary competition: The impact of positive competitor reviews on review credibility and consumer purchase intentions," Journal of Retailing and Consumer Services, Elsevier, vol. 69(C).
    9. Barta, Sergio & Gurrea, Raquel & Flavián, Carlos, 2023. "Consequences of consumer regret with online shopping," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).
    10. Whittaker, Lucas & Kietzmann, Jan & Letheren, Kate & Mulcahy, Rory & Russell-Bennett, Rebekah, 2023. "Brace yourself! Why managers should adopt a synthetic media incident response playbook in an age of falsity and synthetic media," Business Horizons, Elsevier, vol. 66(2), pages 277-290.
    11. Zaman, Mustafeed & Vo-Thanh, Tan & Nguyen, Chi T.K. & Hasan, Rajibul & Akter, Shahriar & Mariani, Marcello & Hikkerova, Lubica, 2023. "Motives for posting fake reviews: Evidence from a cross-cultural comparison," Journal of Business Research, Elsevier, vol. 154(C).
    12. Hajek, Petr & Hikkerova, Lubica & Sahut, Jean-Michel, 2023. "Fake review detection in e-Commerce platforms using aspect-based sentiment analysis," Journal of Business Research, Elsevier, vol. 167(C).
    13. Perez-Castro, A. & Martínez-Torres, M.R. & Toral, S.L., 2023. "Efficiency of automatic text generators for online review content generation," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    14. Birim, Şule Öztürk & Kazancoglu, Ipek & Kumar Mangla, Sachin & Kahraman, Aysun & Kumar, Satish & Kazancoglu, Yigit, 2022. "Detecting fake reviews through topic modelling," Journal of Business Research, Elsevier, vol. 149(C), pages 884-900.

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