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Machine Learning for Authorship Attribution and Cyber Forensics pp 57–74Cite as

Writeprint Mining For Authorship Attribution

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Part of the International Series on Computer Entertainment and Media Technology book series (ISCEMT)

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.

Some contents in this chapter are developed based on the concepts discussed [64] F. Iqbal, R. Hadjidj, B. C. M. Fung, and M. Debbabi, “A novel approach of mining write-prints for authorship attribution in e-mail forensics,” Digit. Investig., vol. 5, pp. S42–S51, 2008.

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Author information

Authors and Affiliations

  1. Zayed University, Abu Dhabi, United Arab Emirates

    Farkhund Iqbal

  2. School of Engineering & Computer Science, Concordia University, Montreal, QC, Canada

    Mourad Debbabi

  3. School of Information Studies, McGill University, Montreal, QC, Canada

    Benjamin C. M. Fung

Authors
  1. Farkhund Iqbal
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  2. Mourad Debbabi
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  3. Benjamin C. M. Fung
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Iqbal, F., Debbabi, M., Fung, B.C.M. (2020). Writeprint Mining For Authorship Attribution. In: Machine Learning for Authorship Attribution and Cyber Forensics. International Series on Computer Entertainment and Media Technology. Springer, Cham. https://doi.org/10.1007/978-3-030-61675-5_5

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