Abusive Language Detection with Graph Convolutional Networks

Pushkar Mishra, Marco Del Tredici, Helen Yannakoudakis, Ekaterina Shutova


Abstract
Abuse on the Internet represents a significant societal problem of our time. Previous research on automated abusive language detection in Twitter has shown that community-based profiling of users is a promising technique for this task. However, existing approaches only capture shallow properties of online communities by modeling follower–following relationships. In contrast, working with graph convolutional networks (GCNs), we present the first approach that captures not only the structure of online communities but also the linguistic behavior of the users within them. We show that such a heterogeneous graph-structured modeling of communities significantly advances the current state of the art in abusive language detection.
Anthology ID:
N19-1221
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2145–2150
Language:
URL:
https://www.aclweb.org/anthology/N19-1221
DOI:
10.18653/v1/N19-1221
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/N19-1221.pdf
Video:
 https://vimeo.com/355811189