‘twazn me!!! ;(’ Automatic Authorship Analysis of Micro-Blogging Messages

  • Rui Sousa Silva
  • Gustavo Laboreiro
  • Luís Sarmento
  • Tim Grant
  • Eugénio Oliveira
  • Belinda Maia
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6716)

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Rui Sousa Silva
    • 1
    • 3
  • Gustavo Laboreiro
    • 2
    • 4
  • Luís Sarmento
    • 2
    • 4
  • Tim Grant
    • 1
  • Eugénio Oliveira
    • 2
  • Belinda Maia
    • 3
  1. 1.Centre for Forensic Linguistics at Aston UniversityUK
  2. 2.Faculdade de Engenharia da Universidade do Porto - DEI - LIACCPortugal
  3. 3.CLUP - Centro de Linguística da Universidade do PortoPortugal
  4. 4.SAPO Labs PortoPortugal

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