摘要
Web 2.0时代,消费者在在线购物、学习和娱乐时越来越多地依赖在线评论信息,而虚假的评论会误导消费者的决策,影响商家的真实信用,因此有效识别虚假评论具有重要意义。文中首先对虚假评论的范围进行了界定,并从虚假评论识别、形成动机、对消费者的影响以及治理策略4个方面归纳了虚假评论的研究内容,给出了虚假评论研究框架和一般识别方法的工作流程。然后从评论文本内容和评论者及其群组行为两个角度,对近十年来国内外的相关研究成果进行了综述,介绍了虚假评论效果评估的相关数据集和评价指标,统计分析了在公开数据集上实现的虚假评论有效识别方法,并从特征选取、模型方法、训练数据集、评价指标值等方面进行了对比分析。最后对虚假评论识别领域的有标注语料规模限制等未来研究方向进行了探讨。
In Web 2.0 era,consumers mostly rely on online reviews from former consumers when they are shopping,learning and entertaining on the Internet.Fake review can mislead users on making consumption decisions and affect the real credit of stores.Therefore,recognizing fake reviews effectively is necessary and meaningful.This paper first starts from the definition of fake review,introduces the research content of false review from four directions,which are fake review recognition,motivation,influence on consumers and how to prevent false review,and then puts forward the research framework of fake reviews and the workflow of general recognition methods.Then it sums up current perspectives of relevant research from the text of fake reviews and fake reviewers,introduces common datasets and evaluation indicators,statistically analyzes the effective recognition method of fake review on open datasets.Specifically,it makes a conclusion about the feature selection,fake review recognition models,training datasets and evaluation indicators of current research works,and makes a comparison among different detection models.Finally,the future research directions of fake review recognition,such as the limit of large scale labeled datasets are discussed.
作者
袁禄
朱郑州
任庭玉
YUAN Lu;ZHU Zheng-zhou;REN Ting-yu(School of Software&Microelectronics,Peking University,Beijing 102600,China)
出处
《计算机科学》
CSCD
北大核心
2021年第1期111-118,共8页
Computer Science
基金
国家重点研发计划项目(2017YFB1402400)。
