TopicSpam: a Topic-Model based approach for spam detection
Published 1 August 2013
Jiwei Li, Claire Cardie, Sujian Li
Citations65
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TL;DR
A generative LDA-based topic modeling approach for fake review detection that can aptly detect the subtle differences between deceptive reviews and truthful ones and achieves about 95% accuracy on review spam datasets, outperforming existing baselines by a large margin.
Abstract
Product reviews are now widely used by individuals and organizations for decision making (Litvin et al., 2008; Jansen, 2010). And because of the profits at stake, people have been known to try to game the system by writing fake reviews to promote target products. As a result, the task of deceptive review detection has been gaining increasing attention. In this paper, we propose a generative LDA-based topic modeling approach for fake review detection. Our model can aptly detect the subtle differences between deceptive reviews and truthful ones and achieves about 95 % accuracy on review spam datasets, outperforming existing baselines by a large margin. 1
Keywords
Computer ScienceSocial Sciences
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