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Text mining and probabilistic language modeling for online review spam detection

Text mining and probabilistic language modeling for online review spam detection Text Mining and Probabilistic Language Modeling for Online Review Spam Detection RAYMOND Y. K. LAU, S. Y. LIAO, and RON CHI-WAI KWOK, City University of Hong Kong KAIQUAN XU, Nanjing University YUNQING XIA, Tsinghua University YUEFENG LI, Queensland University of Technology In the era of Web 2.0, huge volumes of consumer reviews are posted to the Internet every day. Manual approaches to detecting and analyzing fake reviews (i.e., spam) are not practical due to the problem of information overload. However, the design and development of automated methods of detecting fake reviews is a challenging research problem. The main reason is that fake reviews are speci cally composed to mislead readers, so they may appear the same as legitimate reviews (i.e., ham). As a result, discriminatory features that would enable individual reviews to be classi ed as spam or ham may not be available. Guided by the design science research methodology, the main contribution of this study is the design and instantiation of novel computational models for detecting fake reviews. In particular, a novel text mining model is developed and integrated into a semantic language model for the detection of untruthful reviews. The models are then evaluated based on http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Management Information Systems (TMIS) Association for Computing Machinery

Text mining and probabilistic language modeling for online review spam detection

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2011 by ACM Inc.
ISSN
2158-656X
DOI
10.1145/2070710.2070716
Publisher site
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Abstract

Text Mining and Probabilistic Language Modeling for Online Review Spam Detection RAYMOND Y. K. LAU, S. Y. LIAO, and RON CHI-WAI KWOK, City University of Hong Kong KAIQUAN XU, Nanjing University YUNQING XIA, Tsinghua University YUEFENG LI, Queensland University of Technology In the era of Web 2.0, huge volumes of consumer reviews are posted to the Internet every day. Manual approaches to detecting and analyzing fake reviews (i.e., spam) are not practical due to the problem of information overload. However, the design and development of automated methods of detecting fake reviews is a challenging research problem. The main reason is that fake reviews are speci cally composed to mislead readers, so they may appear the same as legitimate reviews (i.e., ham). As a result, discriminatory features that would enable individual reviews to be classi ed as spam or ham may not be available. Guided by the design science research methodology, the main contribution of this study is the design and instantiation of novel computational models for detecting fake reviews. In particular, a novel text mining model is developed and integrated into a semantic language model for the detection of untruthful reviews. The models are then evaluated based on

Journal

ACM Transactions on Management Information Systems (TMIS) – Association for Computing Machinery

Published: Dec 1, 2011

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