Fake Review Detection in E-Commerce: A Comparative Study of Machine Learning Models

DOI

10.2991/978-94-6239-598-5_8How to use a DOI?

Keywords

Fake Reviews; E-commerce Platforms; Machine Learning

Abstract

As a dominant organizational form of the digital economy, e-commerce platforms are increasingly challenged by fake positive reviews and rebate-driven manipulative ratings, which undermine market integrity and distort consumer decision-making. To address this issue, this study collects over 40,000 review samples from Taobao to construct a comprehensive dataset and develops a fake review detection framework by incorporating more than ten machine learning algorithms. The empirical results reveal that the Light Gradient Boosting Machine (LGBM) model demonstrates the highest accuracy in identifying fake reviews. Moreover, the number of fake reviews is positively associated with sales volume, suggesting that merchants possess persistent incentives for review brushing. In addition, the co-movement of fake reviews and negative reviews indicates that manipulative reviews ultimately erode consumer welfare. Building upon these findings, this study proposes several governance strategies for e-commerce platforms, including the establishment of a revised credit evaluation system, enhanced platform transparency, and stricter review monitoring mechanisms. This research contributes to the literature on platform digital governance and provides valuable policy implications for safeguarding consumer welfare in the digital economy.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Cunpu Li
AU  - Chenbo Liu
AU  - Yongfeng Hou
AU  - Yaxuan Lu
AU  - Xiao Liu
AU  - Xinyi Qiu
PY  - 2026
DA  - 2026/02/26
TI  - Fake Review Detection in E-Commerce: A Comparative Study of Machine Learning Models
BT  - Proceedings of the 2025 6th  International Conference on Big Data and Social Sciences (ICBDSS 2025)
PB  - Atlantis Press
SP  - 71
EP  - 80
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-598-5_8
DO  - 10.2991/978-94-6239-598-5_8
ID  - Li2026
ER  -