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High-order concept associations mining and inferential language modeling for online review spam detection

Lai, C. L.; Xu, K. Q.; Lau, Raymond Y. K.; Li, Yuefeng and Song, Dawei (2010). High-order concept associations mining and inferential language modeling for online review spam detection. In: 10th IEEE International Conference on Data Mining Workshops, 14 December 2010, Sydney, Australia.

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DOI (Digital Object Identifier) Link: http://dx.doi.org/10.1109/ICDMW.2010.30
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Abstract

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
pp.1120-1127
Keywords: review spam; spam detection; text mining; language modeling; Kullback-Leibler divergence
Academic Unit/Department: Mathematics, Computing and Technology > Computing & Communications
Item ID: 35287
Depositing User: Mary Mcmahon
Date Deposited: 13 Nov 2012 14:59
Last Modified: 13 Nov 2012 14:59
URI: http://oro.open.ac.uk/id/eprint/35287
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