Content-Based Text Classifiers for Pornographic Web Filtering

Due to the flood of pornographic web sites on the internet, effective Web filtering systems are essential. Web filtering based on content has become one of the important techniques to handle and filter inappropriate information on the web. We examine two machine learning algorithms (support vector machines and Naive Bayes) for pornographic web filtering based on text content. We then focus initially on Thai-language and English-language web sites. In this paper, we aim to investigate whether machine learning algorithms are suitable for web sites classification. The empirical results show that the classifier based support vector machines are more effective for pornographic web filtering than Naive Bayes classifier after testing, especially an effectiveness for the over-blocking problem.