@inproceedings{haber-etal-2023-improving,
title = "Improving the Detection of Multilingual Online Attacks with Rich Social Media Data from {S}ingapore",
author = {Haber, Janosch and
Vidgen, Bertie and
Chapman, Matthew and
Agarwal, Vibhor and
Lee, Roy Ka-Wei and
Yap, Yong Keong and
R{\"o}ttger, Paul},
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.711",
doi = "10.18653/v1/2023.acl-long.711",
pages = "12705--12721",
abstract = "Toxic content is a global problem, but most resources for detecting toxic content are in English. When datasets are created in other languages, they often focus exclusively on one language or dialect. In many cultural and geographical settings, however, it is common to code-mix languages, combining and interchanging them throughout conversations. To shine a light on this practice, and enable more research into code-mixed toxic content, we introduce SOA, a new multilingual dataset of online attacks. Using the multilingual city-state of Singapore as a starting point, we collect a large corpus of Reddit comments in Indonesian, Malay, Singlish, and other languages, and provide fine-grained hierarchical labels for online attacks. We publish the corpus with rich metadata, as well as additional unlabelled data for domain adaptation. We share comprehensive baseline results, show how the metadata can be used for granular error analysis, and demonstrate the benefits of domain adaptation for detecting multilingual online attacks.",
}
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<abstract>Toxic content is a global problem, but most resources for detecting toxic content are in English. When datasets are created in other languages, they often focus exclusively on one language or dialect. In many cultural and geographical settings, however, it is common to code-mix languages, combining and interchanging them throughout conversations. To shine a light on this practice, and enable more research into code-mixed toxic content, we introduce SOA, a new multilingual dataset of online attacks. Using the multilingual city-state of Singapore as a starting point, we collect a large corpus of Reddit comments in Indonesian, Malay, Singlish, and other languages, and provide fine-grained hierarchical labels for online attacks. We publish the corpus with rich metadata, as well as additional unlabelled data for domain adaptation. We share comprehensive baseline results, show how the metadata can be used for granular error analysis, and demonstrate the benefits of domain adaptation for detecting multilingual online attacks.</abstract>
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%0 Conference Proceedings
%T Improving the Detection of Multilingual Online Attacks with Rich Social Media Data from Singapore
%A Haber, Janosch
%A Vidgen, Bertie
%A Chapman, Matthew
%A Agarwal, Vibhor
%A Lee, Roy Ka-Wei
%A Yap, Yong Keong
%A Röttger, Paul
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F haber-etal-2023-improving
%X Toxic content is a global problem, but most resources for detecting toxic content are in English. When datasets are created in other languages, they often focus exclusively on one language or dialect. In many cultural and geographical settings, however, it is common to code-mix languages, combining and interchanging them throughout conversations. To shine a light on this practice, and enable more research into code-mixed toxic content, we introduce SOA, a new multilingual dataset of online attacks. Using the multilingual city-state of Singapore as a starting point, we collect a large corpus of Reddit comments in Indonesian, Malay, Singlish, and other languages, and provide fine-grained hierarchical labels for online attacks. We publish the corpus with rich metadata, as well as additional unlabelled data for domain adaptation. We share comprehensive baseline results, show how the metadata can be used for granular error analysis, and demonstrate the benefits of domain adaptation for detecting multilingual online attacks.
%R 10.18653/v1/2023.acl-long.711
%U https://aclanthology.org/2023.acl-long.711
%U https://doi.org/10.18653/v1/2023.acl-long.711
%P 12705-12721
Markdown (Informal)
[Improving the Detection of Multilingual Online Attacks with Rich Social Media Data from Singapore](https://aclanthology.org/2023.acl-long.711) (Haber et al., ACL 2023)
ACL