Gender-Preferential Text Mining of E-mail Discourse

This paper describes an investigation of authorship gender attribution mining from e-mail text documents. We used an extended set of predominantly topic content-free e-mail document features such as style markers, structural characteristics and gender-preferential language features together with a Support Vector Machine learning algorithm. Experiments using a corpus of e-mail documents generated by a large number of authors of both genders gave promising results for author gender categorisation.

• Type: Conference
• Year: 2002
• Where: ACSAC
• Authors: Malcolm Corney, Olivier Y. de Vel, Alison Anderson, George M. Mohay
• Keywords: ACSAC 2002, Authorship Gender Attribution, E-mail Documents, Security Privacy, Topic Content-free E-mail