Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/1014
Title: L-HSAB: A Levantine Twitter Dataset for Hate Speech and Abusive Language
Authors: Mulki, Hala
Haddad, Hatem
Ali, Chedi Bechikh
Alshabani, Halima
Keywords: AGREEMENT
Issue Date: 2019
Publisher: ASSOC COMPUTATIONAL LINGUISTICS-ACL
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 (alpha) indicated the consistency of the annotations.
Description: 3rd Workshop on Abusive Language Online -- AUG 01, 2019 -- Florence, ITALY
URI: https://hdl.handle.net/20.500.13091/1014
ISBN: 978-1-950737-43-7
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections

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