Ranking Right-Wing Extremist Social Media Profiles by Similarity to Democratic and Extremist Groups

• Matthias Hartung ^[1] ; Roman Klinger ^[2] ; Franziska Schmidtke ^[3] ; Lars Vogel ^[3]
  1. [1] Bielefeld University

     Bielefeld University

     Kreisfreie Stadt Bielefeld, Alemania

     
  2. [2] University of Stuttgart

     University of Stuttgart

     Stadtkreis Stuttgart, Alemania

     
  3. [3] Friedrich Schiller University Jena

     Friedrich Schiller University Jena

     Kreisfreie Stadt Jena, Alemania

     

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• Localización: 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis WASSA 2017: Proceedings of the Workshop / Alexandra Balahur Dobrescu (ed. lit.), Saif M. Mohammad (ed. lit.), Erik van der Goot (ed. lit.), 2017, ISBN 978-1-945626-95-1, págs. 24-33
• Idioma: inglés
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• Resumen

  • Social media are used by an increasing number of political actors. A small subset of these is interested in pursuing extrem- ist motives such as mobilization, recruit- ing or radicalization activities. In order to counteract these trends, online providers and state institutions reinforce their mon- itoring efforts, mostly relying on manual workflows. We propose a machine learn- ing approach to support manual attempts towards identifying right-wing extremist content in German Twitter profiles. Based on a fine-grained conceptualization of right- wing extremism, we frame the task as rank- ing each individual profile on a continuum spanning different degrees of right-wing extremism, based on a nearest neighbour approach. A quantitative evaluation reveals that our ranking model yields robust per- formance (up to 0.81 F1 score) when being used for predicting discrete class labels. At the same time, the model provides plausi- ble continuous ranking scores for a small sample of borderline cases at the division of right-wing extremism and New Right political movements.