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Contribution to Book
Writeprint Mining For Authorship Attribution
Machine Learning for Authorship Attribution and Cyber Forensics
  • Farkhund Iqbal
  • Mourad Debbabi
  • Benjamin C. M. Fung
Document Type
Book Chapter
Publication Date
12-5-2020
Abstract

This chapter presents a novel approach to frequent-pattern based Writeprint creation, and addresses two authorship problems: authorship attribution in the usual way (disregarding stylistic variation), and authorship attribution by focusing on stylistic variations. Stylistic variation is the occasional change in the writing features of an individual, with respect to the type of recipient and the topic of a message. The authorship methods proposed in this chapter and in the following chapters are applicable to different types of online messages; however, for the purposes of experimentation, an e-mail corpus has been used in this chapter, to demonstrate the efficacy of said methods.

Publisher
Springer International Publishing
Disciplines
Indexed in Scopus
No
Open Access
No
https://doi.org/10.1007/978-3-030-61675-5_5
Citation Information
Farkhund Iqbal, Mourad Debbabi and Benjamin C. M. Fung. "Writeprint Mining For Authorship Attribution" Machine Learning for Authorship Attribution and Cyber Forensics (2020) p. 57 - 74 ISSN: <a href="https://v2.sherpa.ac.uk/id/publication/issn/2364-9488" target="_blank">2364-9488</a>
Available at: http://works.bepress.com/farkhund-iqbal/105/