By analyzing a set of access attempts by teenagers to pornographic websites, we found that more than half of them are image searches and visits to websites with little text information. It is obvious that textual content-based filters cannot correctly categorize such access attempts. This paper describes a novel URLbased objectionable content categorization approach and its application to web filtering. In this approach, we break the URL into a sequence of n-grams with a range of n's and then a machine learning algorithm is applied to the n-gram representation of URLs to learn a classifier of pornographic websites. We showed empirically that the URL-based approach is able to correctly identify many of the objectionable web pages. We also demonstrated that the optimum web filtering results could be achieved when it was used with a content-based approach in a production environment.

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