@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://aclanthology.org/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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Improving Moderation of Online Discussions via Interpretable Neural Models
%A Švec, Andrej
%A Pikuliak, Matúš
%A Šimko, Marián
%A Bieliková, Mária
%Y Fišer, Darja
%Y Huang, Ruihong
%Y Prabhakaran, Vinodkumar
%Y Voigt, Rob
%Y Waseem, Zeerak
%Y Wernimont, Jacqueline
%S Proceedings of the 2nd Workshop on Abusive Language Online (ALW2)
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F svec-etal-2018-improving
%X 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.
%R 10.18653/v1/W18-5108
%U https://aclanthology.org/W18-5108
%U https://doi.org/10.18653/v1/W18-5108
%P 60-65
Markdown (Informal)
[Improving Moderation of Online Discussions via Interpretable Neural Models](https://aclanthology.org/W18-5108) (Švec et al., ALW 2018)
ACL