Analyzing and Detecting Review Spam
Analyzing and Detecting Review Spam
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DOI：
10.1109/icdm.2007.68
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发表时间：
2007-10
期刊：
影响因子：
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通讯作者：
Nitin Jindal;B. Liu
中科院分区：
文献类型：
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作者：
Nitin Jindal;B. Liu
从产品评论、论坛帖子和博客中挖掘意见是一个重要的研究课题，具有许多应用。然而，现有的研究一直集中在这些来源的意见提取，分类和总结。到目前为止，一个尚未研究的重要问题是意见垃圾或在线意见的可信度。在本文中，我们研究这个问题的背景下，产品评论。据我们所知，目前还没有关于这一主题的公开研究，尽管垃圾网页和垃圾邮件已经被广泛研究。我们将看到评论垃圾邮件与Web页面垃圾邮件和电子邮件垃圾邮件有很大的不同，因此需要不同的检测技术。基于对来自amazon.com的580万条评论和214万条评论者的分析，我们发现评论垃圾邮件很普遍。在本文中，我们首先提出了一个分类的垃圾评论，然后提出了几种技术来检测它们。
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.