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

Authors:
Rui Sousa Silva;Gustavo Laboreiro;Luís Sarmento;Tim Grant;Eugénio Oliveira;Belinda Maia
Affiliations:
Centre for Forensic Linguistics at Aston University and Centro de Linguística da Universidade do Porto;Faculdade de Engenharia da Universidade do Porto, DEI, LIACC and SAPO Labs Porto;Faculdade de Engenharia da Universidade do Porto, DEI, LIACC and SAPO Labs Porto;Centre for Forensic Linguistics at Aston University;Faculdade de Engenharia da Universidade do Porto, DEI, LIACC;Centro de Linguística da Universidade do Porto
Venue:
NLDB'11 Proceedings of the 16th international conference on Natural language processing and information systems
Year:
2011

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Abstract

In this paper we propose a set of stylistic markers for automatically attributing authorship to micro-blogging messages. The proposed 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 discriminate 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.