Unsupervised Spam Detection Based on String Alienness Measures
Unsupervised Spam Detection Based on String Alienness Measures
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DOI：
10.1007/978-3-540-75488-6_16
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发表时间：
2007-10
期刊：
影响因子：
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通讯作者：
K. Narisawa;H. Bannai;Kohei Hatano;M. Takeda
中科院分区：
文献类型：
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作者：
K. Narisawa;H. Bannai;Kohei Hatano;M. Takeda
我们提出了一种基于字符串的等价关系从给定文档集中检测垃圾邮件文档的无监督方法。我们给出了三种度量来量化文档中子字符串的异质性（即它们与其他子字符串的差异程度）。如果文档包含属于高度陌生的等价类的子字符串，则该文档将被分类为垃圾邮件。所提出的方法是无监督的、独立于语言的且可扩展的。对从日本网络论坛收集的数据进行的计算实验表明，该方法成功地发现了垃圾邮件。
We propose an unsupervised method for detecting spam documents from a given set of documents, based onequivalence relations on strings. We give three measures for quantifying thealienness(i.e. how different they are from others) of substrings within the documents. A document is then classified as spam if it contains a substring that is in an equivalence class with a high degree of alienness. The proposed method is unsupervised, language independent, and scalable. Computational experiments conducted on data collected from Japanese web forums show that the method successfully discovers spams.