Abstract
A method for filtering spam blogs (splogs) based on a machine learning technique, and its evaluation results are described. Today, spam blogs (splogs) became one of major issues on the Web. The problem of splogs is that values of blog sites are different by people. We propose a novel user-oriented splog filtering method that can adapt each user’s preference for valuable blogs. We use the SVM(Support Vector Machine) for creating a personalized splog filter for each user. We had two experiments: (1) an experiment of individual splog judgement, and (2) an experiment for user oriented splog filtering. From the former experiment, we found existence of ‘gray’ blogs that are needed to treat by persons. From the latter experiment, we found that we can provide appropriate personalized filters by choosing the best feature set for each user. An overview of proposed method, and evaluation results are described.
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Yoshinaka, T., Ishii, S., Fukuhara, T., Masuda, H., Nakagawa, H. (2010). A User-Oriented Splog Filtering Based on a Machine Learning. In: Breslin, J.G., Burg, T.N., Kim, HG., Raftery, T., Schmidt, JH. (eds) Recent Trends and Developments in Social Software. BlogTalk BlogTalk 2008 2009. Lecture Notes in Computer Science, vol 6045. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16581-8_9
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DOI: https://doi.org/10.1007/978-3-642-16581-8_9
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