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        <title>L-HSAB: A Levantine Twitter Dataset for Hate Speech and Abusive Language</title>
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            <title>Proceedings of the Third Workshop on Abusive Language Online</title>
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            <namePart type="given">Sarah</namePart>
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            <namePart type="given">Vinodkumar</namePart>
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    <abstract>Hate speech and abusive language have become a common phenomenon on Arabic social media. Automatic hate speech and abusive detection systems can facilitate the prohibition of toxic textual contents. The complexity, informality and ambiguity of the Arabic dialects hindered the provision of the needed resources for Arabic abusive/hate speech detection research. In this paper, we introduce the first publicly-available Levantine Hate Speech and Abusive (L-HSAB) Twitter dataset with the objective to be a benchmark dataset for automatic detection of online Levantine toxic contents. We, further, provide a detailed review of the data collection steps and how we design the annotation guidelines such that a reliable dataset annotation is guaranteed. This has been later emphasized through the comprehensive evaluation of the annotations as the annotation agreement metrics of Cohen’s Kappa (k) and Krippendorff’s alpha (α) indicated the consistency of the annotations.</abstract>
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%0 Conference Proceedings
%T L-HSAB: A Levantine Twitter Dataset for Hate Speech and Abusive Language
%A Mulki, Hala
%A Haddad, Hatem
%A Bechikh Ali, Chedi
%A Alshabani, Halima
%Y Roberts, Sarah T.
%Y Tetreault, Joel
%Y Prabhakaran, Vinodkumar
%Y Waseem, Zeerak
%S Proceedings of the Third Workshop on Abusive Language Online
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F mulki-etal-2019-l
%X Hate speech and abusive language have become a common phenomenon on Arabic social media. Automatic hate speech and abusive detection systems can facilitate the prohibition of toxic textual contents. The complexity, informality and ambiguity of the Arabic dialects hindered the provision of the needed resources for Arabic abusive/hate speech detection research. In this paper, we introduce the first publicly-available Levantine Hate Speech and Abusive (L-HSAB) Twitter dataset with the objective to be a benchmark dataset for automatic detection of online Levantine toxic contents. We, further, provide a detailed review of the data collection steps and how we design the annotation guidelines such that a reliable dataset annotation is guaranteed. This has been later emphasized through the comprehensive evaluation of the annotations as the annotation agreement metrics of Cohen’s Kappa (k) and Krippendorff’s alpha (α) indicated the consistency of the annotations.
%R 10.18653/v1/W19-3512
%U https://aclanthology.org/W19-3512
%U https://doi.org/10.18653/v1/W19-3512
%P 111-118

Markdown (Informal)

[L-HSAB: A Levantine Twitter Dataset for Hate Speech and Abusive Language](https://aclanthology.org/W19-3512) (Mulki et al., ALW 2019)

• L-HSAB: A Levantine Twitter Dataset for Hate Speech and Abusive Language (Mulki et al., ALW 2019)

ACL

• Hala Mulki, Hatem Haddad, Chedi Bechikh Ali, and Halima Alshabani. 2019. L-HSAB: A Levantine Twitter Dataset for Hate Speech and Abusive Language. In Proceedings of the Third Workshop on Abusive Language Online, pages 111–118, Florence, Italy. Association for Computational Linguistics.