hPSD: A Hybrid PU-Learning-Based Spammer Detection Model for Product Reviews
- 2 November 2018
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Cybernetics
- Vol. 50 (4), 1595-1606
- https://doi.org/10.1109/tcyb.2018.2877161
Abstract
Spammers, who manipulate online reviews to promote or suppress products, are flooding in online commerce. To combat this trend, there has been a great deal of research focused on detecting review spammers, most of which design diversified features and thus develop various classifiers. The widespread growth of crowdsourcing platforms has created large-scale deceptive review writers who behave more like normal users, that the way they can more easily evade detection by the classifiers that are purely based on fixed characteristics. In this paper, we propose a hybrid semisupervised learning model titled hybrid PU-learning-based spammer detection (hPSD) for spammer detection to leverage both the users' characteristics and the user-product relations. Specifically, the hPSD model can iteratively detect multitype spammers by injecting different positive samples, and allows the construction of classifiers in a semisupervised hybrid learning framework. Comprehensive experiments on movie dataset with shilling injection confirm the superior performance of hPSD over existing baseline methods. The hPSD is then utilized to detect the hidden spammers from real-life Amazon data. A set of spammers and their underlying employers (e.g., book publishers) are successfully discovered and validated. These demonstrate that hPSD meets the real-world application scenarios and can thus effectively detect the potentially deceptive review writers.Keywords
Funding Information
- National Basic Research Program of China (2016YFB1000901)
- National Natural Science Foundation of China (71571093, 91646204, 71701089, 71801123)
- National Center for International Joint Research on E-Business Information Processing (2013B01035)
- National Natural Science Foundation of China (71725002, 71531001, U1636210, 71471009)
- Fundamental Research Funds for the Central Universities
This publication has 31 references indexed in Scilit:
- Collective Spammer Detection in Evolving Multi-Relational Social NetworksPublished by Association for Computing Machinery (ACM) ,2015
- Uncovering Crowdsourced Manipulation of Online ReviewsPublished by Association for Computing Machinery (ACM) ,2015
- Estimating the prevalence of deception in online review communitiesPublished by Association for Computing Machinery (ACM) ,2012
- Shilling Attack Detection—A New Approach for a Trustworthy Recommender SystemINFORMS Journal on Computing, 2012
- Impact of Online Consumer Reviews on Sales: The Moderating Role of Product and Consumer CharacteristicsJournal of Marketing, 2010
- Positive Unlabeled Learning for Data Stream ClassificationPublished by Society for Industrial & Applied Mathematics (SIAM) ,2009
- Examining the Relationship Between Reviews and Sales: The Role of Reviewer Identity Disclosure in Electronic MarketsInformation Systems Research, 2008
- Unsupervised strategies for shilling detection and robust collaborative filteringUser Modelling and User-Adapted Interaction, 2008
- Building text classifiers using positive and unlabeled examplesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- Learning Rate Adaptation in Stochastic Gradient DescentNonconvex Optimization and Its Applications, 2001