On the limited memory BFGS method for large scale optimization
Mathematical Programming: Series A and B
Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th international conference on World Wide Web
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
MailRank: using ranking for spam detection
Proceedings of the 14th ACM international conference on Information and knowledge management
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Topical TrustRank: using topicality to combat web spam
Proceedings of the 15th international conference on World Wide Web
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Proceedings of the 5th International Workshop on Adversarial Information Retrieval on the Web
Social influence analysis in large-scale networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Detecting spam blogs: a machine learning approach
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Proceedings of the 2010 ACM conference on Computer supported cooperative work
Mining advisor-advisee relationships from research publication networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Detecting product review spammers using rating behaviors
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Finding unusual review patterns using unexpected rules
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Distortion as a validation criterion in the identification of suspicious reviews
Proceedings of the First Workshop on Social Media Analytics
Finding deceptive opinion spam by any stretch of the imagination
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Learning to infer social ties in large networks
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Who will follow you back?: reciprocal relationship prediction
Proceedings of the 20th ACM international conference on Information and knowledge management
Review Graph Based Online Store Review Spammer Detection
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Spotting fake reviewer groups in consumer reviews
Proceedings of the 21st international conference on World Wide Web
Cross-lingual knowledge linking across wiki knowledge bases
Proceedings of the 21st international conference on World Wide Web
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Learning to identify review spam
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
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Review spamming is quite common on many online shopping platforms like Amazon. Previous attempts for fake review and spammer detection use features of reviewer behavior, rating, and review content. However, to the best of our knowledge, there is no work capable of detecting fake reviews and review spammers at the same time. In this paper, we propose an algorithm to achieve the two goals simultaneously. By defining features to describe each review and reviewer, a Review Factor Graph model is proposed to incorporate all the features and to leverage belief propagation between reviews and reviewers. Experimental results show that our algorithm outperforms all of the other baseline methods significantly with respect to both efficiency and accuracy.