Computer Science ›› 2021, Vol. 48 ›› Issue (1): 111-118.doi: 10.11896/jsjkx.200500101

Special Issue: Big Data & Data Scinece

• Database & Big Data & Data Science • Previous Articles     Next Articles

Survey on Fake Review Recognition

YUAN Lu, ZHU Zheng-zhou, REN Ting-yu   

  1. School of Software & Microelectronics,Peking University,Beijing 102600,China
  • Received:2020-05-21 Revised:2020-08-22 Online:2021-01-15 Published:2021-01-15
  • About author:YUAN Lu,born in 1996,postgraduate,is a member of China Computer Federation.Her main research interests include machine learning and natural language processing.
    ZHU Zheng-zhou,born in 1979,Ph.D,associate professor,is a member of China Computer Federation.His main research interests include learning resource recommendation and knowledge graph.
  • Supported by:
    National Key Research and Development Program of China(2017YFB1402400).

Abstract: 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.

Key words: Behavior feature, Fake review, Fake review recognition, Fake reviewer, Textual feature

CLC Number: 

  • TP391
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