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基于机器学习的虚假评论识别与效用评估模型

Fake review identification and utility evaluation model using machine learning.

作者信息

Choi Wonil, Nam Kyungmin, Park Minwoo, Yang Seoyi, Hwang Sangyoon, Oh Hayoung

机构信息

Department of Business Administration, Sungkyunkwan University, Seoul, South Korea.

College of Computing and Informatics, Sungkyunkwan University, Seoul, South Korea.

出版信息

Front Artif Intell. 2023 Jan 19;5:1064371. doi: 10.3389/frai.2022.1064371. eCollection 2022.

DOI:10.3389/frai.2022.1064371
PMID:36744111
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9893788/
Abstract

Due to the structural growth of e-commerce platforms, the frequency of exchange of opinions and the number of online reviews of platform participants related to products are increasing. However, given the growth of fake reviews, the corresponding growth in the quality of online reviews seems to be slow, at best. The number of cases of harm to retailers and customers caused by malicious false reviews is steadily increasing every year. In this context, it is becoming difficult for users to determine useful reviews amid a flood of information. As a result, the intrinsic value of online reviews that reduce uncertainty in pre-purchase decisions is blurred, and e-commerce platforms are on the verge of losing credibility and traffic. Through this study, we intend to present solutions related to review filtering and classification by constructing a model for judging the authenticity and usefulness of online reviews using machine learning.

摘要

由于电子商务平台的结构性增长，与产品相关的平台参与者意见交流频率和在线评论数量不断增加。然而，鉴于虚假评论的增多，在线评论质量的相应增长充其量似乎较为缓慢。恶意虚假评论给零售商和顾客造成损害的案例数量每年都在稳步上升。在这种背景下，用户在大量信息中很难辨别出有用的评论。结果，能够降低购买前决策不确定性的在线评论的内在价值变得模糊不清，电子商务平台正面临失去信誉和流量的风险。通过本研究，我们打算构建一个利用机器学习判断在线评论真实性和有用性的模型，从而提出与评论过滤和分类相关的解决方案。

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