WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Scoring products from reviews through application of fuzzy techniques
Expert Systems with Applications: An International Journal
Foundations and Trends in Information Retrieval
Multi-facets quality assessment of online opinionated expressions
WISS'10 Proceedings of the 2010 international conference on Web information systems engineering
Text mining and probabilistic language modeling for online review spam detection
ACM Transactions on Management Information Systems (TMIS)
Quality and Leniency in Online Collaborative Rating Systems
ACM Transactions on the Web (TWEB)
Semi-automatic semantic moderation of web annotations
Proceedings of the 21st international conference companion on World Wide Web
Evaluating Arabic spam classifiers using link analysis
Proceedings of the 3rd International Conference on Information and Communication Systems
Content-based analysis to detect Arabic web spam
Journal of Information Science
Discovering business intelligence from online product reviews: A rule-induction framework
Expert Systems with Applications: An International Journal
Information Retrieval in the Commentsphere
ACM Transactions on Intelligent Systems and Technology (TIST)
Fake reviews: the malicious perspective
NLDB'12 Proceedings of the 17th international conference on Applications of Natural Language Processing and Information Systems
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Mining of opinions from product reviews, forum posts and blogs is an important research topic with many applications. However, existing research has been focused on extraction, classification and summarization of opinions from these sources. An important issue that has not been studied so far is the opinion spam or the trustworthiness of online opinions. In this paper, we study this issue in the context of product reviews. To our knowledge, there is still no published study on this topic, although Web page spam and email spam have been investigated extensively. We will see that review spam is quite different from Web page spam and email spam, and thus requires different detection techniques. Based on the analysis of 5.8 million reviews and 2.14 million reviewers from amazon.com, we show that review spam is widespread. In this paper, we first present a categorization of spam reviews and then propose several techniques to detect them.