Toward A Language Modeling Approach for Consumer Review Spam Detection
Numerous reports have indicated the severity of fake reviews (i.e., spam) posted to various e-Commerce or opinion sharing Web sites. Nevertheless, very few studies have been conducted to examine the trustworthiness of online consumer reviews because of the lack of an effective computational methodology. Unlike other kinds of Web spam, untruthful reviews could just look like other legitimate reviews (i.e., ham), and so it is difficult to apply any features to distinguish the two classes. One main contribution of our research work is the development of a novel computational methodology to combat online review spam. Our experimental results confirm that the KL divergence and the probabilistic language modeling based computational model is effective for the detection of untruthful reviews. Empowered by the proposed computational methods, our empirical study found that around 2％ of the consumer reviews posted to a large e-Commerce site is spam.
Language Models Kullback-Leibler Divergence Review Spam Spam Detection Electronic Commerce.
C.L. Lai K.Q. Xu Raymond Y.K. Lau Y. Li L. Jing
Department of Information Systems City University of Hong Kong Tat Chee Avenue, Kowloon, Hong Kong S School of Information Technology Queensland University of Technology GPO Box 2434, Brisbane, Qld 400 School of Computer and Information Technology Beijing Jiaotong University, China
国际会议
2010 IEEE International Conference on e-Business Engineering(2010年电子商务工程国际研讨会 ICEBE 2010)
上海
英文
1-8
2010-11-10（万方平台首次上网日期，不代表论文的发表时间）