@inproceedings{svec-etal-2018-improving,
    title = "Improving Moderation of Online Discussions via Interpretable Neural Models",
    author = "{\v{S}}vec, Andrej  and
      Pikuliak, Mat{\'u}{\v{s}}  and
      {\v{S}}imko, Mari{\'a}n  and
      Bielikov{\'a}, M{\'a}ria",
    editor = "Fi{\v{s}}er, Darja  and
      Huang, Ruihong  and
      Prabhakaran, Vinodkumar  and
      Voigt, Rob  and
      Waseem, Zeerak  and
      Wernimont, Jacqueline",
    booktitle = "Proceedings of the 2nd Workshop on Abusive Language Online ({ALW}2)",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/W18-5108/",
    doi = "10.18653/v1/W18-5108",
    pages = "60--65",
    abstract = "Growing amount of comments make online discussions difficult to moderate by human moderators only. Antisocial behavior is a common occurrence that often discourages other users from participating in discussion. We propose a neural network based method that partially automates the moderation process. It consists of two steps. First, we detect inappropriate comments for moderators to see. Second, we highlight inappropriate parts within these comments to make the moderation faster. We evaluated our method on data from a major Slovak news discussion platform."
}