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What makes deceptive online reviews? A linguistic analysis perspective

Author

Listed:
  • Wen Zhang

    (Beijing University of Technology)

  • Qiang Wang

    (Wenzhou University of Technology)

  • Jian Li

    (Beijing University of Technology)

  • Zhenzhong Ma

    (University of Windsor)

  • Gokul Bhandari

    (University of Windsor)

  • Rui Peng

    (Beijing University of Technology)

Abstract

With the rapid development of e-commerce, online reviews have become an important information source for consumers and e-commerce businesses. While the negative impact of deceptive online reviews has been well recognized, more research has to be done to help understand the linguistic manifestations of deceptive online reviews in order to help identify deceptive reviews and help increase the value and sustainability of e-commerce businesses. This study explores the linguistic manifestations of deceptive online reviews based on the reality monitoring theory, and then uses the data from Amazon.com online product reviews to examine perceptual cues, affective cues, detail cues, relevance cues, and cognitive cues of various deceptive online reviews. The results show that reviews for emotional catharsis are more extreme with affective cues, while perfunctory reviews often lack details with fewer prepositions and adjectives. In addition, deceptive reviews often lack relevance cues when these reviews are made to obtain the rewards provided by the vendors while paid posters tend to use more cognitive cues in deceptive reviews. Moreover, deceptive online reviews under all motives often lack perceptual cues. These findings provide a deeper understanding of the linguistic manifestations of deceptive online reviews and provide significant managerial implications for e-commerce businesses to employ high-quality online reviews for sustainable growth.

Suggested Citation

  • Wen Zhang & Qiang Wang & Jian Li & Zhenzhong Ma & Gokul Bhandari & Rui Peng, 2023. "What makes deceptive online reviews? A linguistic analysis perspective," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-14, December.
    • Handle: RePEc:pal:palcom:v:10:y:2023:i:1:d:10.1057_s41599-023-02295-5
    DOI: 10.1057/s41599-023-02295-5
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    1. 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).
    2. Dina Mayzlin & Yaniv Dover & Judith Chevalier, 2014. "Promotional Reviews: An Empirical Investigation of Online Review Manipulation," American Economic Review, American Economic Association, vol. 104(8), pages 2421-2455, August.
    3. Zhihong Ke & De Liu & Daniel J. Brass, 2020. "Do Online Friends Bring Out the Best in Us? The Effect of Friend Contributions on Online Review Provision," Information Systems Research, INFORMS, vol. 31(4), pages 1322-1336, December.
    4. Pang, Hua & Liu, Jun & Lu, Jiahui, 2022. "Tackling fake news in socially mediated public spheres: A comparison of Weibo and WeChat," Technology in Society, Elsevier, vol. 70(C).
    5. Zuo, Wenming & Bai, Weijing & Zhu, Wenfeng & He, Xinming & Qiu, Xinxin, 2022. "Changes in service quality of sharing accommodation: Evidence from airbnb," Technology in Society, Elsevier, vol. 71(C).
    6. Kapoor, Payal S. & M S, Balaji & Maity, Moutusy & Jain, Nikunj Kumar, 2021. "Why consumers exaggerate in online reviews? Moral disengagement and dark personality traits," Journal of Retailing and Consumer Services, Elsevier, vol. 60(C).
    7. Nadine Dijkstra & Stephen M. Fleming, 2023. "Subjective signal strength distinguishes reality from imagination," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    8. Yi, Jisu & Oh, Yun Kyung, 2022. "The informational value of multi-attribute online consumer reviews: A text mining approach," Journal of Retailing and Consumer Services, Elsevier, vol. 65(C).
    9. Dong, Xuefan & Lian, Ying, 2021. "A review of social media-based public opinion analyses: Challenges and recommendations," Technology in Society, Elsevier, vol. 67(C).
    10. Daria Plotkina & Andréas Munzel & Jessie Pallud, 2020. "Illusions of truth - Experimental insights into human and algorithmic detections of fake online reviews," Post-Print hal-04077201, HAL.
    11. Ansari, Sana & Gupta, Sumeet, 2021. "Customer perception of the deceptiveness of online product reviews: A speech act theory perspective," International Journal of Information Management, Elsevier, vol. 57(C).
    12. Zhanfei Lei & Dezhi Yin & Han Zhang, 2021. "Focus Within or On Others: The Impact of Reviewers’ Attentional Focus on Review Helpfulness," Information Systems Research, INFORMS, vol. 32(3), pages 801-819, September.
    13. Tunç, Murat & Cavusoglu, Huseyin & Raghunathan, Srinivasan, 2021. "Online Product Reviews: Is a Finer-Grained Rating Scheme Superior to a Coarser One?," Other publications TiSEM ec57cbf3-7415-4427-aafc-6, Tilburg University, School of Economics and Management.
    14. Huang, Guanxiong & Liang, Hai, 2021. "Uncovering the effects of textual features on trustworthiness of online consumer reviews: A computational-experimental approach," Journal of Business Research, Elsevier, vol. 126(C), pages 1-11.
    15. Liu, Angela Xia & Xie, Ying & Zhang, Jurui, 2019. "It's Not Just What You Say, But How You Say It: The Effect of Language Style Matching on Perceived Quality of Consumer Reviews," Journal of Interactive Marketing, Elsevier, vol. 46(C), pages 70-86.
    16. Chatterjee, Swagato & Goyal, Divesh & Prakash, Atul & Sharma, Jiwan, 2021. "Exploring healthcare/health-product ecommerce satisfaction: A text mining and machine learning application," Journal of Business Research, Elsevier, vol. 131(C), pages 815-825.
    17. Dandan Qiao & Shun-Yang Lee & Andrew B. Whinston & Qiang Wei, 2020. "Financial Incentives Dampen Altruism in Online Prosocial Contributions: A Study of Online Reviews," Information Systems Research, INFORMS, vol. 31(4), pages 1361-1375, December.
    18. Li, Liangqiang & Yang, Liang & Zhao, Meng & Liao, Miyan & Cao, Yunzhong, 2022. "Exploring the success determinants of crowdfunding for cultural and creative projects: An empirical study based on signal theory," Technology in Society, Elsevier, vol. 70(C).
    19. Uttara M. Ananthakrishnan & Beibei Li & Michael D. Smith, 2020. "A Tangled Web: Should Online Review Portals Display Fraudulent Reviews?," Information Systems Research, INFORMS, vol. 31(3), pages 950-971, September.
    20. Jiawei Chen & Yinghui (Catherine) Yang & Hongyan Liu, 2021. "Mining Bilateral Reviews for Online Transaction Prediction: A Relational Topic Modeling Approach," Information Systems Research, INFORMS, vol. 32(2), pages 541-560, June.
    21. Lamb, Yasmin & Cai, Wenjie & McKenna, Brad, 2020. "Exploring the complexity of the individualistic culture through social exchange in online reviews," International Journal of Information Management, Elsevier, vol. 54(C).
    22. Stefan Gössling & C. Michael Hall & Ann-Christin Andersson, 2018. "The manager's dilemma: a conceptualization of online review manipulation strategies," Current Issues in Tourism, Taylor & Francis Journals, vol. 21(5), pages 484-503, March.
    23. Banerjee, Snehasish, 2022. "Exaggeration in fake vs. authentic online reviews for luxury and budget hotels," International Journal of Information Management, Elsevier, vol. 62(C).
    24. Yang Wang & Paulo Goes & Zaiyan Wei & Daniel Zeng, 2019. "Production of Online Word‐of‐Mouth: Peer Effects and the Moderation of User Characteristics," Production and Operations Management, Production and Operations Management Society, vol. 28(7), pages 1621-1640, July.
    25. Zhuang, Mengzhou & Cui, Geng & Peng, Ling, 2018. "Manufactured opinions: The effect of manipulating online product reviews," Journal of Business Research, Elsevier, vol. 87(C), pages 24-35.
    26. 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.
    27. Angela Aerry Choi & Daegon Cho & Dobin Yim & Jae Yun Moon & Wonseok Oh, 2019. "When Seeing Helps Believing: The Interactive Effects of Previews and Reviews on E-Book Purchases," Information Systems Research, INFORMS, vol. 30(4), pages 1164-1183, December.
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