Malcolm Corney, Alison Anderson and George Mohay
Queensland University of Technology
Olivier de Vel
Defence Science and Technology Organisation
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.
Keywords: computer forensics, authorship attribution, email, data mining
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