| Title | Modern Stylometry: A Review & Experimentation with Machine Learning |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Muldoon, Connagh, Ikram, Ahsan, Khan Mirza, Qublai Ali |
| Conference Name | 2021 8th International Conference on Future Internet of Things and Cloud (FiCloud) |
| Keywords | Analytical models, artificial intelligence, cloud computing, Communication systems, Human Behavior, Internet of Things, machine learning, Measurement, Metrics, pubcrawl, stylometry |
| 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. |
| DOI | 10.1109/FiCloud49777.2021.00049 |
| Citation Key | muldoon_modern_2021 |
Modern Stylometry: A Review & Experimentation with Machine Learning