An Evaluation Of Authorship Attribution Using Random Forests

Authors/Creators

Mahmoud Khonji
Youssef Iraqi
Andrew Jones, Edith Cowan University

Abstract

Electronic text (e-text) stylometry aims at identifying the writing style of authors of electronic texts, such as electronic documents, blog posts, tweets, etc. Identifying such styles is quite attractive for identifying authors of disputed e-text, identifying their profile attributes (e.g. gender, age group, etc), or even enhancing services such as search engines and recommender systems. Despite the success of Random Forests, its performance has not been evaluated on Author Attribtion problems. In this paper, we present an evaluation of Random Forests in the problem domain of Authorship Attribution. Additionally, we have taken advantage of Random Forests' robustness against noisy features by extracting a diverse set of features from evaluated e-texts. Interestingly, the resultant model achieved the highest classification accuracy in all problems, except one where it misclassified only a single instance.

RAS ID

20833

Document Type

Conference Proceeding

Date of Publication

2015

Faculty

Faculty of Health, Engineering and Science

School

School of Computer and Security Science

Copyright

subscription content

Publisher

IEEE

Comments

Khonji, M., Iraqi, Y., & Jones, A. (2015, May). An evaluation of authorship attribution using Random Forests. In Information and Communication Technology Research (ICTRC), 2015 International Conference on (pp. 68-71). IEEE. Available here

Recommended Citation

Khonji, M., Iraqi, Y., & Jones, A. (2015). An Evaluation Of Authorship Attribution Using Random Forests. DOI: https://doi.org/10.1109/ICTRC.2015.7156423