Similarity ranking using handcrafted stylometric traits in a swedish context

Abstract

In this paper we introduce a new type of handcrafted textual features called stylometric traits, used to create a stylistic writeprint of an author's writing style. These can be divided into four categories: (i) word variations, (ii) abbreviations, (iii) internet jargon, and (iv) numbers. A similarity ranking method is developed for ranking users' social media accounts based on how similar their writeprints are. We experiment with both vector distance metrics and machine learning-based class probabilities to measure similarity. The best performance is achieved using stylometric traits combined with the Jensen-Shannon distance metric, outperforming traditional stylometric features used in previous research.