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Anomalous User Comment Detection in Social News Websites

  • Jorge de-la-Peña-Sordo
  • Iker Pastor-López
  • Xabier Ugarte-Pedrero
  • Igor Santos
  • Pablo García Bringas
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 299)

Abstract

The Web has evolved over the years and, now, not only the administrators of a site generate content. Users of a website can express themselves showing their feelings or opinions. This fact has led to negative side effects: sometimes the content generated is inappropriate. Frequently, this content is authored by troll users who deliberately seek controversy. In this paper we propose a new method to detect trolling comments in social news websites. To this end, we extract a combination of statistical, syntactic and opinion features from the user comments. Since this troll phenomenon is quite common in the web, we propose a novel experimental setup for our anomaly detection method: considering troll comments as base model (normal behaviour: ‘normality’). We evaluate our approach with data from ‘Menéame’, a popular Spanish social news site, showing that our method can obtain high rates whilst minimising the labelling task.

Keywords

Information Retrieval Troll Detection Web Categorisation Content Filtering Machine-Learning 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Jorge de-la-Peña-Sordo
    • 1
  • Iker Pastor-López
    • 1
  • Xabier Ugarte-Pedrero
    • 1
  • Igor Santos
    • 1
  • Pablo García Bringas
    • 1
  1. 1.S3Lab, DeustoTech ComputingUniversity of DeustoBilbaoSpain

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