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.- Anthology ID:
- 2021.argmining-1.13
- Volume:
- Proceedings of the 8th Workshop on Argument Mining
- Month:
- November
- Year:
- 2021
- Address:
- Punta Cana, Dominican Republic
- Venue:
- ArgMining
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 133–141
- Language:
- URL:
- https://aclanthology.org/2021.argmining-1.13
- DOI:
- 10.18653/v1/2021.argmining-1.13
- Cite (ACL):
- Neele Falk, Iman Jundi, Eva Maria Vecchi, and Gabriella Lapesa. 2021. Predicting Moderation of Deliberative Arguments: Is Argument Quality the Key?. In Proceedings of the 8th Workshop on Argument Mining, pages 133–141, Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Predicting Moderation of Deliberative Arguments: Is Argument Quality the Key? (Falk et al., ArgMining 2021)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2021.argmining-1.13.pdf