Improving Moderation of Online Discussions via Interpretable Neural Models

Andrej Švec, Matúš Pikuliak, Marián Šimko, Mária Bieliková


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.
Anthology ID:
W18-5108
Volume:
Proceedings of the 2nd Workshop on Abusive Language Online (ALW2)
Month:
October
Year:
2018
Address:
Brussels, Belgium
Editors:
Darja Fišer, Ruihong Huang, Vinodkumar Prabhakaran, Rob Voigt, Zeerak Waseem, Jacqueline Wernimont
Venue:
ALW
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
60–65
Language:
URL:
https://aclanthology.org/W18-5108
DOI:
10.18653/v1/W18-5108
Bibkey:
Cite (ACL):
Andrej Švec, Matúš Pikuliak, Marián Šimko, and Mária Bieliková. 2018. Improving Moderation of Online Discussions via Interpretable Neural Models. In Proceedings of the 2nd Workshop on Abusive Language Online (ALW2), pages 60–65, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Improving Moderation of Online Discussions via Interpretable Neural Models (Švec et al., ALW 2018)
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PDF:
https://aclanthology.org/W18-5108.pdf