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Social bookmarking systems such as BibSonomy and del.icio.us have become increasingly popular with the prevalent use of internet. These systems provide powerful infrastructure solutions for semantic annotation and information sharing, promoting diverse kinds of internet-based activities, e.g., web exploration, creating and joining web-based communities, and buying recently published volumes. This paper proposes a machine learning-based approach to automatic spam detection. In specific, a set of relevant features, i.e., the number of posts and posted tags for each user are extracted from training data. The extracted tags are sorted by mutual information. Then, the tags, having high mutual information value and used in test data, are chosen for the classification task.

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Format:PDFSize:128 KB
Date:Aug 2008
Pages:6

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