Deep Learning for User Comment Moderation

John Pavlopoulos, Prodromos Malakasiotis, Ion Androutsopoulos


Abstract
Experimenting with a new dataset of 1.6M user comments from a Greek news portal and existing datasets of EnglishWikipedia comments, we show that an RNN outperforms the previous state of the art in moderation. A deep, classification-specific attention mechanism improves further the overall performance of the RNN. We also compare against a CNN and a word-list baseline, considering both fully automatic and semi-automatic moderation.
Anthology ID:
W17-3004
Volume:
Proceedings of the First Workshop on Abusive Language Online
Month:
August
Year:
2017
Address:
Vancouver, BC, Canada
Editors:
Zeerak Waseem, Wendy Hui Kyong Chung, Dirk Hovy, Joel Tetreault
Venue:
ALW
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
25–35
Language:
URL:
https://aclanthology.org/W17-3004
DOI:
10.18653/v1/W17-3004
Bibkey:
Cite (ACL):
John Pavlopoulos, Prodromos Malakasiotis, and Ion Androutsopoulos. 2017. Deep Learning for User Comment Moderation. In Proceedings of the First Workshop on Abusive Language Online, pages 25–35, Vancouver, BC, Canada. Association for Computational Linguistics.
Cite (Informal):
Deep Learning for User Comment Moderation (Pavlopoulos et al., ALW 2017)
Copy Citation:
PDF:
https://aclanthology.org/W17-3004.pdf