Online Review Spam Detection by New Linguistic Features
With the fast growing and importance of online reviews, malicious users start to abuse the online review websites and deliberately post low quality, untrustworthy, or even fraudulent reviews, which are typically referred to as ``spam reviews''. Many existing studies on review spam detection are based on classification models. Features such as the number of verbs used in the reviews are commonly used to construct the spam review classification model. Surprisingly, many linguistic features of users' reviews have not been thoroughly considered for review spam detection. In this paper, we focus on different types of linguistic features and evaluate their performance on detecting spam reviews. Our empirical evaluation conducted on a spam review benchmark dataset validated the proposed features significantly improve the performance of online review spam detection, reaching more than 93\% accuracy.
Proceedings of the iConference 2015, 2015.
© The Authors, 2015
Karami A., Zhou, B. (2015). Online review spam detection by new linguistic features. Proceedings of the iConference 2015, Newport Beach, CA. http://hdl.handle.net/2142/73749