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
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
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
Learning to identify review spam
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
PIKM 2013: the 6th ACM workshop for ph.d. students in information and knowledge management
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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In this paper, we first define our research problem as to detect collusive spammers in online review communities. Next we present our current progress on this topic, in which we have spotted anomalies by evaluating 15 behavioral features proposed in the state-of-the-art approaches. Then we propose a novel hybrid classification/clustering method to detect colluders in our dataset based on selected informative features. Experimental results show that our method promisingly improve the performance of traditional classifiers by incorporating clustering for the smoothing. Finally, possible extensions of our current work and challenges in achieving them are discussed as our future directions.