Attribution of authorship in instant messaging software applications, based on similarity measures of the stylometric features’ vector

Abstract

This paper describes the issue of authorship attribution based on the content of conversations originating from instant messaging software applications. The results presented in the paper refer to the corpus of conversations conducted in Polish. On the basis of a standardised model of the corpus of conversations, stylometric features were extracted, which were divided into four groups: word and message length distributions, character frequencies, tf-idf matrix and features extracted on the basis of turns (conversational features). The vectors of users’ stylometric features were compared in pairs by using Euclidean, cosine and Manhattan metrics. CMC curves were used to analyse the significance of the feature groups and the effectiveness of the metrics for identifying similar speech styles. The best results were obtained by the group of features being the tf-idf matrix compared with the use of cosine distance and the group of features extracted on the basis of turns compared with the use of the Manhattan metric.