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Detecting fake reviews with supervised machine learning algorithms

Author

Listed:
  • Minwoo Lee
  • Young Ho Song
  • Lin Li
  • Kyung Young Lee
  • Sung-Byung Yang

Abstract

This study provides an applicable methodological procedure applying Artificial Intelligence (AI)-based supervised Machine Learning (ML) algorithms in detecting fake reviews of online review platforms and identifies the best ML algorithm as well as the most critical fake review determinants for a given restaurant review dataset. Our empirical findings from analyzing 16 determinants (review-related, reviewer-related, and linguistic attributes) measured from over 43,000 online restaurant reviews reveal that among the seven ML algorithms, the random forest algorithm outperforms the other algorithms and, among the 16 review attributes, time distance is found to be the most important, followed by two linguistic (affective and cognitive cues) and two review-related attributes (review depth and structure). The present study contributes to the literature on fake online review detection, especially in the hospitality field and the body of knowledge on supervised ML algorithms.

Suggested Citation

  • Minwoo Lee & Young Ho Song & Lin Li & Kyung Young Lee & Sung-Byung Yang, 2022. "Detecting fake reviews with supervised machine learning algorithms," The Service Industries Journal, Taylor & Francis Journals, vol. 42(13-14), pages 1101-1121, October.
    • Handle: RePEc:taf:servic:v:42:y:2022:i:13-14:p:1101-1121
    DOI: 10.1080/02642069.2022.2054996
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    Cited by:

    1. Guetz, Bernhard & Bidmon, Sonja, 2023. "The Credibility of Physician Rating Websites: A Systematic Literature Review," Health Policy, Elsevier, vol. 132(C).

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