The steady growth and popularization of the Web increases the competition between the websites and creates opportunities for profit in several segments. Thus, there is a great interest in keeping the website in a good position in search results. The problem is that many websites use techniques to circumvent the search engines which deteriorates the search results and exposes users to dangerous content. Given this scenario, this paper presents a performance evaluation of different models of artificial neural networks to automatically classify web spam.We have conducted an empirical experiment using a well-known, large and public web spam database. The results indicate that the evaluated approaches outperform the state-of-the-art web spam filters. © Springer-Verlag Berlin Heidelberg 2012.
CITATION STYLE
Silva, R. M., Almeida, T. A., & Yamakami, A. (2012). Towards web spam filtering with neural-based approaches. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7637 LNAI, pp. 199–209). Springer Verlag. https://doi.org/10.1007/978-3-642-34654-5_21
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