Learning to Classify Threaten E-mail

Abstract

In this paper we study supervised classification of e-mails. We consider the task of threaten e-mail detection (i.e. email related to terrorism, fraud, etc.). In this supervised learning setting, we investigate the use of data mining classifiers for automatic threaten e-mail detection. We show that decision tree is a good choice for this task as it runs fast on large and high dimensional databases, is easy to tune and is highly accurate, outperforming popular algorithms such as support vector machines, Naive Bayes. In particular, we are interested in detecting fraudulent and possibly criminal activities from such e-mails.