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Man vs machine – Detecting deception in online reviews

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  • Petrescu, Maria
  • Ajjan, Haya
  • Harrison, Dana L.

Abstract

This study focused on three main research objectives: analyzing the methods used to identify deceptive online consumer reviews, evaluating insights provided by multi-method automated approaches based on individual and aggregated review data, and formulating a review interpretation framework for identifying deception. The theoretical framework is based on two critical deception-related models, information manipulation theory and self-presentation theory. The findings confirm the interchangeable characteristics of the various automated text analysis methods in drawing insights about review characteristics and underline their significant complementary aspects. An integrative multi-method model that approaches the data at the individual and aggregate level provides more complex insights regarding the quantity and quality of review information, sentiment, cues about its relevance and contextual information, perceptual aspects, and cognitive material.

Suggested Citation

  • Petrescu, Maria & Ajjan, Haya & Harrison, Dana L., 2023. "Man vs machine – Detecting deception in online reviews," Journal of Business Research, Elsevier, vol. 154(C).
    • Handle: RePEc:eee:jbrese:v:154:y:2023:i:c:s0148296322008116
    DOI: 10.1016/j.jbusres.2022.113346
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    References listed on IDEAS

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    1. 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).

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