@inproceedings{falk-etal-2021-predicting,
    title = "Predicting Moderation of Deliberative Arguments: Is Argument Quality the Key?",
    author = "Falk, Neele  and
      Jundi, Iman  and
      Vecchi, Eva Maria  and
      Lapesa, Gabriella",
    editor = "Al-Khatib, Khalid  and
      Hou, Yufang  and
      Stede, Manfred",
    booktitle = "Proceedings of the 8th Workshop on Argument Mining",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.argmining-1.13/",
    doi = "10.18653/v1/2021.argmining-1.13",
    pages = "133--141",
    abstract = "Human moderation is commonly employed in deliberative contexts (argumentation and discussion targeting a shared decision on an issue relevant to a group, e.g., citizens arguing on how to employ a shared budget). As the scale of discussion enlarges in online settings, the overall discussion quality risks to drop and moderation becomes more important to assist participants in having a cooperative and productive interaction. The scale also makes it more important to employ NLP methods for(semi-)automatic moderation, e.g. to prioritize when moderation is most needed. In this work, we make the first steps towards (semi-)automatic moderation by using state-of-the-art classification models to predict which posts require moderation, showing that while the task is undoubtedly difficult, performance is significantly above baseline. We further investigate whether argument quality is a key indicator of the need for moderation, showing that surprisingly, high quality arguments also trigger moderation. We make our code and data publicly available."
}