Writeprint Mining For Authorship Attribution
Document Type
Book Chapter
Source of Publication
Machine Learning for Authorship Attribution and Cyber Forensics
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.
DOI Link
ISSN
Publisher
Springer International Publishing
First Page
57
Last Page
74
Disciplines
Computer Sciences
Recommended Citation
Iqbal, Farkhund; Debbabi, Mourad; and Fung, Benjamin C. M., "Writeprint Mining For Authorship Attribution" (2020). All Works. 4024.
https://zuscholars.zu.ac.ae/works/4024
Indexed in Scopus
no
Open Access
no