Detecting fake reviews with supervised machine learning algorithms

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.

摘要

本研究将基于人工智能(AI)的监督式机器学习(ML)算法应用于在线评论平台的虚假评论检测，并为给定的餐厅评论数据集识别出最佳的机器学习算法和最关键的虚假评论决定因素。我们分析了从43,000多条在线餐厅评论中得出的16个决定因素(评论相关，评论者相关和语言属性), 实证结果表明，在7种机器学习算法中，随机森林算法优于其他算法，在16种评论属性中，时间距离是最重要的，其次是两个语言属性(情感和认知)和两个评论相关属性(评论深度和结构)。本研究对虚假在线评论检测方面的文献做出了贡献，特别是在酒店领域和监督式机器学习方面的知识体系。

KEYWORDS:

• Fake review
• detection model development
• online review platform
• supervised machine learning
• artificial intelligence
• business intelligence

关键词:

• 虚假评论
• 检测模型开发
• 在线评论平台
• 监督式机器学习
• 人工智能
• 商业智能

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2019S1A3A2098438).