C4.5: programs for machine learning
C4.5: programs for machine learning
Topical TrustRank: using topicality to combat web spam
Proceedings of the 15th international conference on World Wide Web
Detecting spam web pages through content analysis
Proceedings of the 15th international conference on World Wide Web
Utility scoring of product reviews
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Detecting product review spammers using rating behaviors
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Detecting product review spammers using rating behaviors
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Proceedings of the 20th international conference companion on World wide web
Text mining and probabilistic language modeling for online review spam detection
ACM Transactions on Management Information Systems (TMIS)
Spotting fake reviewer groups in consumer reviews
Proceedings of the 21st international conference on World Wide Web
Serf and turf: crowdturfing for fun and profit
Proceedings of the 21st international conference on World Wide Web
Review spam detection via time series pattern discovery
Proceedings of the 21st international conference companion on World Wide Web
Identify Online Store Review Spammers via Social Review Graph
ACM Transactions on Intelligent Systems and Technology (TIST)
Review spam detection via temporal pattern discovery
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
A generic approach to generate opinion lists of phrases for opinion mining applications
Proceedings of the First International Workshop on Issues of Sentiment Discovery and Opinion Mining
Discovering K web user groups with specific aspect interests
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
Fake reviews: the malicious perspective
NLDB'12 Proceedings of the 17th international conference on Applications of Natural Language Processing and Information Systems
Knowledge discovery interestingness measures based on unexpectedness
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Syntactic stylometry for deception detection
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
NordSec'12 Proceedings of the 17th Nordic conference on Secure IT Systems
Simultaneously detecting fake reviews and review spammers using factor graph model
Proceedings of the 5th Annual ACM Web Science Conference
Spotting opinion spammers using behavioral footprints
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Review spam detector with rating consistency check
Proceedings of the 51st ACM Southeast Conference
What are you complaining about?: a study of online reviews of mobile applications
BCS-HCI '13 Proceedings of the 27th International BCS Human Computer Interaction Conference
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In recent years, opinion mining attracted a great deal of research attention. However, limited work has been done on detecting opinion spam (or fake reviews). The problem is analogous to spam in Web search [1, 9 11]. However, review spam is harder to detect because it is very hard, if not impossible, to recognize fake reviews by manually reading them [2]. This paper deals with a restricted problem, i.e., identifying unusual review patterns which can represent suspicious behaviors of reviewers. We formulate the problem as finding unexpected rules. The technique is domain independent. Using the technique, we analyzed an Amazon.com review dataset and found many unexpected rules and rule groups which indicate spam activities.