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Content analysis of fake consumer reviews by survey-based text categorization

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  • Moon, Sangkil
  • Kim, Moon-Yong
  • Iacobucci, Dawn

Abstract

As the influence of online consumer reviews grows, deceptive reviews are a worsening problem, betraying consumers' trust in reviews by pretending to be authentic and informative. This research identifies factors that can separate deceptive reviews from genuine ones. First, we create a novel means of detection by contrasting authentic versus fake word patterns specific to a given domain (e.g., hotel services). We use a survey on a crowdsourcing platform to obtain both genuine and deceptive reviews of hotels. We learned the word patterns from each category to discriminate genuine reviews from fake ones for positively and negatively evaluated reviews, respectively. We show that our All Terms procedure outperforms current benchmark methods in computational linguistics and marketing. Our extended analysis reveals the factors that determine fake reviews (e.g., a lack of details, present- and future-time orientation, and emotional exaggeration) and the factors influencing people's willingness to write fake reviews (including social media trust, product quality consciousness, deal proneness, hedonic and utilitarian consumption, prosocial behavior, and individualism). We also use our procedure to analyze more than 250,000 real-world hotel reviews to detect fake reviews and identify the hotel and review characteristics influencing review fakery in the industry (e.g., star rating, franchise hotel, hotel size, room price, review timing, and review rating).

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  • Moon, Sangkil & Kim, Moon-Yong & Iacobucci, Dawn, 2021. "Content analysis of fake consumer reviews by survey-based text categorization," International Journal of Research in Marketing, Elsevier, vol. 38(2), pages 343-364.
    • Handle: RePEc:eee:ijrema:v:38:y:2021:i:2:p:343-364
    DOI: 10.1016/j.ijresmar.2020.08.001
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    5. Kim, Jong Min & Park, Keeyeon Ki-cheon & Mariani, Marcello & Wamba, Samuel Fosso, 2024. "Investigating reviewers' intentions to post fake vs. authentic reviews based on behavioral linguistic features," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    6. 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).
    7. Román, Sergio & Riquelme, Isabel P. & Iacobucci, Dawn, 2023. "Fake or credible? Antecedents and consequences of perceived credibility in exaggerated online reviews," Journal of Business Research, Elsevier, vol. 156(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. Hajek, Petr & Sahut, Jean-Michel, 2022. "Mining behavioural and sentiment-dependent linguistic patterns from restaurant reviews for fake review detection," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    10. Ko, Eunhee Emily & Bowman, Douglas, 2023. "Suspicious online product reviews: An empirical analysis of brand and product characteristics using Amazon data," International Journal of Research in Marketing, Elsevier, vol. 40(4), pages 898-911.
    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).
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