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We propose an unsupervised method for detecting spam documents from Web page data, based on equivalence relations on strings. We propose 3 measures for quantifying the alienness (i.e. how different it... is from others) of substring equivalence classes within a given set of strings. A document is then classified as spam if it contains a characteristic equivalence class as a substring. The proposed method is unsupervised, independent of language, and is very efficient. Computational experiments conducted on data collected from Japanese web forums show fairly good results.show more
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