@inproceedings{mishra-etal-2019-abusive,
title = "{A}busive {L}anguage {D}etection with {G}raph {C}onvolutional {N}etworks",
author = "Mishra, Pushkar and
Del Tredici, Marco and
Yannakoudakis, Helen and
Shutova, Ekaterina",
editor = "Burstein, Jill and
Doran, Christy and
Solorio, Thamar",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1221",
doi = "10.18653/v1/N19-1221",
pages = "2145--2150",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Abusive Language Detection with Graph Convolutional Networks
%A Mishra, Pushkar
%A Del Tredici, Marco
%A Yannakoudakis, Helen
%A Shutova, Ekaterina
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S 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)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F mishra-etal-2019-abusive
%X 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.
%R 10.18653/v1/N19-1221
%U https://aclanthology.org/N19-1221
%U https://doi.org/10.18653/v1/N19-1221
%P 2145-2150
Markdown (Informal)
[Abusive Language Detection with Graph Convolutional Networks](https://aclanthology.org/N19-1221) (Mishra et al., NAACL 2019)
ACL
- Pushkar Mishra, Marco Del Tredici, Helen Yannakoudakis, and Ekaterina Shutova. 2019. Abusive Language Detection with Graph Convolutional Networks. In 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), pages 2145–2150, Minneapolis, Minnesota. Association for Computational Linguistics.