@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://aclanthology.org/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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Predicting Moderation of Deliberative Arguments: Is Argument Quality the Key?
%A Falk, Neele
%A Jundi, Iman
%A Vecchi, Eva Maria
%A Lapesa, Gabriella
%Y Al-Khatib, Khalid
%Y Hou, Yufang
%Y Stede, Manfred
%S Proceedings of the 8th Workshop on Argument Mining
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F falk-etal-2021-predicting
%X 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.
%R 10.18653/v1/2021.argmining-1.13
%U https://aclanthology.org/2021.argmining-1.13
%U https://doi.org/10.18653/v1/2021.argmining-1.13
%P 133-141
Markdown (Informal)
[Predicting Moderation of Deliberative Arguments: Is Argument Quality the Key?](https://aclanthology.org/2021.argmining-1.13) (Falk et al., ArgMining 2021)
ACL