Title :
Analyzing and Detecting Review Spam
Author :
Jindal, Nitin ; Liu, Bing
Author_Institution :
Univ. of Illinois at Chicago, Chicago
Abstract :
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.
Keywords :
Internet; data mining; electronic commerce; Web page spam; email spam; product review spam detection; product reviews opinion mining; spam review categorization; Application software; Blogs; Computer science; Data analysis; Data mining; Manufacturing; Search engines; Unsolicited electronic mail; User-generated content; Web pages;
Conference_Titel :
Data Mining, 2007. ICDM 2007. Seventh IEEE International Conference on
Conference_Location :
Omaha, NE
Print_ISBN :
978-0-7695-3018-5
DOI :
10.1109/ICDM.2007.68