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Improvised explosive device web pages represent a significant source of knowledge for security organizations. These web pages exist in distinctive genres of communication, providing different types and levels of information for the intelligence community. This paper presents a framework for the classification of improvised explosive device web pages by genre. The approach uses a complex feature extractor, extended feature representation, and support vector machine learning algorithms. Improvised explosive device web pages were collected from the Dark Web and two classification models were examined, one using feature selection. Classification accuracy exceeded 88%.