On authorship attribution: document’s length may not matter

Abstract The task of authorship attribution is a classification problem where a given text is analyzed by a machine learning algorithm with the goal of verifying its author. This task has great application in the world of forensic analysis and intellectual property problems. In this work, we propose to use Support Vector Machines paired with Term Frequency – Inverse Term Frequency to solve authorship attribution in cases where the author is part of a predetermined set of authors, called a closed set. This method could also be applied to large language models in order to find out if those have a “style” of writing. In the end, we show how we compared our results with related works and how we managed to improve the results on a well known benchmark dataset by 6.4 %.