Identifying the Authors of Suspect Email

Abstract

In this paper, we present the results of an investigation into identifying the authorship of email messages by analysis of the contents and style of the email messages themselves. A set of stylistics features applicable to text in general and an extended set of email-specific structural features were identified. A Support Vector Machine learning method was used to discriminate between the authorship classes. Through a series of baseline experiments on non-email data, it was found that approximately 20 email messages with approximately 100 words in each message should be sufficient to discriminate authorship in most cases. These results were confirmed with a corpus of email data and performance was further enhanced when a set of email-specific features were added. This outcome has important implications in the management of such problems as email abuse, anonymous email messages and computer forensics.