twazn me!!! ;(’ automatic authorship analysis of micro-blogging messages

Abstract. In this paper we propose a set of stylistic markers for auto-matically attributing authorship to micro-blogging messages. The pro-posed markers include highly personal and idiosyncratic editing options, such as ‘emoticons’, interjections, punctuation, abbreviations and other low-level features. We evaluate the ability of these features to help dis-criminate the authorship of Twitter messages among three authors. For that purpose, we train SVM classifiers to learn stylometric models for each author based on different combinations of the groups of stylistic features that we propose. Results show a relatively good-performance in attributing authorship of micro-blogging messages (F = 0.63) using this set of features, even when training the classifiers with as few as 60 examples from each author (F = 0.54). Additionally, we conclude that emoticons are the most discriminating features in these groups.