Modern Stylometry: A Review & Experimentation with Machine Learning

Muldoon, Connagh, Ikram, Ahsan and Ali Mirza, Qublai Khan ORCID: 0000-0003-3403-2935 (2021) Modern Stylometry: A Review & Experimentation with Machine Learning. In: 2021 8th International Conference on Future Internet of Things and Cloud (FiCloud). IEEE, pp. 293-298. ISBN 9781665425759

Text (Peer Reviewed Version) 10770 Ali Mirza et al (2021) Modern_Stylometry_A_Review_amp_Experimentation_with_Machine_Learning.pdf - Accepted Version Restricted to Repository staff only Available under License All Rights Reserved. Download (372kB)

Abstract

The problem of authorship attribution has applications from literary studies (such as the great Shakespeare/Marlowe debates) to counter-intelligence. The field of stylometry aims to offer quantitative results for authorship attribution. In this paper, we present a combination of stylometric techniques using machine learning. An implementation of the system is used to analyse chat logs and attempts to construct a stylometric model for users within the presented chat system. This allows for the authorship attribution of other works they may write under different names or within different communication systems. This implementation demonstrates accuracy of up to 84 % across the dataset, a full 34 % increase against a random-choice control baseline.

Item Type:	Book Section
Uncontrolled Keywords:	Machine Learning; Stylometry; Artificial Intelligence
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Divisions:	Schools and Research Institutes > School of Business, Computing and Social Sciences
Research Priority Areas:	Applied Business & Technology
Depositing User:	Kate Greenaway
Date Deposited:	03 Mar 2022 12:13
Last Modified:	31 Oct 2023 12:25
URI:	https://eprints.glos.ac.uk/id/eprint/10770

University Staff: Request a correction | Repository Editors: Update this record