2013 SyntheticReviewSpammingandDefen

Subject Headings: Spam Filtering, Spam Review.

Notes

Online reviews have been popularly adopted in many applications. Since they can either promote or harm the reputation of a product or a service, buying and selling fake reviews becomes a profitable business and a big threat. In this paper, we introduce a very simple, but powerful review spamming technique that could fail the existing feature-based detection algorithms easily. It uses one truthful review as a template, and replaces its sentences with those from other reviews in a repository. Fake reviews generated by this mechanism are extremely hard to detect: Both the state-of-the-art computational approaches and human readers acquire an error rate of 35%-48%, just slightly better than a random guess. While it is challenging to detect such fake reviews, we have made solid progress in suppressing them. A novel defense method that leverages the difference of semantic flows between synthetic and truthful reviews is developed, which is able to reduce the detection error rate to approximately 22%, a significant improvement over the performance of existing approaches. Nevertheless, it is still a challenging research task to further decrease the error rate.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2013 SyntheticReviewSpammingandDefen	Xifeng Yan Huan Sun Alex Morales			Synthetic Review Spamming and Defense				10.1145/2487575.2487688		2013