Mining e-mail content for author identification forensics
We describe an investigation into e-mail content mining for author identification, or authorship attribution, for the purpose of forensic investigation. We focus our discussion on the ability to discriminate between authors for the case of both aggregated e-mail topics as well as across different email topics. An extended set of e-mail document features including structural characteristics and linguistic patterns were derived and, together with a Support Vector Machine learning algorithm, were used for mining the e-mail content. Experiments using a number of e-mail documents generated by different authors on a set of topics gave promising results for both aggregated and multi-topic author categorisation.
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|Item Type:||Journal Article|
|Additional Information:||For more information, please refer to the publisher's website (link above) or contact the author: firstname.lastname@example.org|
|Keywords:||email analysis, computer forensics, support vector machine, text analysis, computational linguistics, stylistics|
|Subjects:||Australian and New Zealand Standard Research Classification > LANGUAGES COMMUNICATION AND CULTURE (200000) > LINGUISTICS (200400) > Computational Linguistics (200402)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Pattern Recognition and Data Mining (080109)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Natural Language Processing (080107)
|Divisions:||Past > Schools > Computer Science
Past > QUT Faculties & Divisions > Faculty of Science and Technology
Past > Institutes > Information Security Institute
|Copyright Owner:||Copyright 2001 Please consult the authors.|
|Deposited On:||06 Jun 2007|
|Last Modified:||23 Dec 2014 22:13|
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