Abstract
We aim to develop a technique to detect search engine optimization (SEO) spam websites. Specifically, we propose four methods for extracting the SEO spam entries from a given trackback network in blogspace that are based on fundamental metrics on a network. Using real data of trackback networks in blogspace, we experimentally evaluate the performance of the proposed methods, and demonstrate that the method of ranking entries based on average degrees of nearest neighbors can be a very promising approach for extracting SEO spam entries.
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© 2005 Springer-Verlag Berlin Heidelberg
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Kimura, M., Saito, K., Kazama, K., Sato, Sy. (2005). Detecting Search Engine Spam from a Trackback Network in Blogspace. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3684. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11554028_101
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DOI: https://doi.org/10.1007/11554028_101
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28897-8
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