Modern stylometry: A review & experimentation with machine learning

Muldoon, Connagh; Ikram, Ahsan; Khan Mirza, Qublai Ali

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.

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2021 8th International Conference on Future Internet of Things and Cloud (FiCloud)
• Start Date: Aug 23, 2021
• End Date: Aug 25, 2021
• Acceptance Date: Aug 1, 2021
• Publication Date: Nov 9, 2021
• Deposit Date: Feb 27, 2023
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Pages: 293-298
• Book Title: 2021 8th International Conference on Future Internet of Things and Cloud (FiCloud), 2021
• ISBN: 978-1-6654-2575-9
• DOI: https://doi.org/10.1109/FiCloud49777.2021.00049
• Keywords: Machine learning, stylometry, artificial intelligence, Measurement, Cloud computing, Analytical models, Communication systems, Machine learning, Internet of Things
• Public URL: https://uwe-repository.worktribe.com/output/10477868
• Publisher URL: https://ieeexplore.ieee.org/document/9590327
• Related Public URLs: https://ieeexplore.ieee.org/xpl/conhome/9590197/proceeding