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Study on Spammer Detection Based on Reviewer-Specific Characteristics
Received date: 2015-03-13
Revised date: 2015-05-02
Online published: 2015-05-20
[Purpose/significance]This paper mainly studies the problem of review spammer detection based on specific characteristics. It tends to reveal the characteristics and behavioral regularities of Water Army on the web, build a simple and reasonable explanation prediction model of identifying review spammers, to build a foundation for the deep reviews mining research.[Method/process]By integrating empirical analysis and Machine Learning (ML) techniques, it explores the inner evaluation strategies of the target website. Factor analysis is employed to extract the features and behavior-specific factors of reviewers, on the basis of which a Logistic Regression model is built to identify review spammer.[Result/conclusion]On the dataset built from the target website, the classification accuracy for spammer identification can achieve to 73.8%,and AUC measure is 80.9%. Additionally, three feature factors related with reviewers, contribution, activity and word- specific literacy are proved to be significantly associated with the identification of reviewer, while the opposite result is get for reviewers' level, emotion and rating deviation. Basically, it gets the same results by using ML based methods and empirical analysis and the reasonable explanation can be given for our prediction model.
Key words： review spammer; review spam; behavior characteristics
Nie Hui , Wu Yijun . Study on Spammer Detection Based on Reviewer-Specific Characteristics[J]. Library and Information Service, 2015 , 59(10) : 102 -109 . DOI: 10.13266/j.issn.0252-3116.2015.10.015
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