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.
| Translated title of the contribution | Detecting fake reviews with supervised machine learning algorithms |
|---|---|
| Original language | English |
| Pages (from-to) | 1101-1121 |
| Number of pages | 21 |
| Journal | Service Industries Journal |
| Volume | 42 |
| Issue number | 13-14 |
| DOIs | |
| State | Published - 2022 |
Bibliographical note
Publisher Copyright:© 2022 Informa UK Limited, trading as Taylor & Francis Group.
Keywords
- Fake review
- artificial intelligence
- business intelligence
- detection model development
- online review platform
- supervised machine learning
ASJC Scopus subject areas
- Strategy and Management
- Management of Technology and Innovation