Yoshihiko Suhara
ABSTRACT
Spammers use a wide range of content generation techniques with low quality pages known as content spam to achieve their goals. We argue that content spam must be tackled using a wide range of content quality features. In this paper, we propose novel sentence-level diversity features based on the probabilistic topic model. We combine them with other content features to build a content spam classifier. Our experiments show that our method outperforms the conventional methods.
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Digital Library
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Index Terms
- Automatically generated spam detection based on sentence-level topic information
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