Lai, C. L.; Xu, K. Q.; Lau, Raymond Y. K.; Li, Yuefeng and Song, Dawei
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|DOI (Digital Object Identifier) Link:||http://doi.org/10.1109/ICDMW.2010.30|
|Google Scholar:||Look up in Google Scholar|
Despite many incidents about fake online consumer reviews have been reported, very few studies have been conducted to date to examine the trustworthiness of online consumer reviews. One of the reasons is the lack of an effective computational method to separate the untruthful reviews (i.e., spam) from the legitimate ones (i.e., ham) given the fact that prominent spam features are often missing in online reviews. The main contribution of our research work is the development of a novel review spam detection method which is underpinned by an unsupervised inferential language modeling framework. Another contribution of this work is the development of a high-order concept association mining method which provides the essential term association knowledge to bootstrap the performance for untruthful review detection. Our experimental results confirm that the proposed inferential language model equipped with high-order concept association knowledge is effective in untruthful review detection when compared with other baseline methods.
|Item Type:||Conference Item|
|Copyright Holders:||2010 IEEE|
|Extra Information:||ISBN: 978-0-7695-4257-7
|Keywords:||review spam; spam detection; text mining; language modeling; Kullback-Leibler divergence|
|Academic Unit/Department:||Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
|Depositing User:||Mary Mcmahon|
|Date Deposited:||13 Nov 2012 14:59|
|Last Modified:||04 Oct 2016 11:22|
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