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Title:Online Review Spam Detection by New Linguistic Features
Author(s):Karami, Amir; Zhou, Bin
Subject(s):data analytics and evaluation
text/data/knowledge mining
Abstract: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.
Issue Date:2015-03-15
Publisher:iSchools
Series/Report:iConference 2015 Proceedings
Genre:Conference Poster
Type:Text
Language:English
URI:http://hdl.handle.net/2142/73749
Peer Reviewed:yes
Rights Information:Copyright 2015 is held by the authors. Copyright permissions, when appropriate, must be obtained directly from the authors.
Date Available in IDEALS:2015-03-24


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