Ara-Women-Hate: An Annotated Corpus Dedicated to Hate Speech Detection against Women in the Arabic Community

Imane Guellil, Ahsan Adeel, Faical Azouaou, Mohamed Boubred, Yousra Houichi, Akram Abdelhaq Moumna


Abstract
In this paper, an approach for hate speech detection against women in the Arabic community on social media (e.g. Youtube) is proposed. In the literature, similar works have been presented for other languages such as English. However, to the best of our knowledge, not much work has been conducted in the Arabic language. A new hate speech corpus (Arabic_fr_en) is developed using three different annotators. For corpus validation, three different machine learning algorithms are used, including deep Convolutional Neural Network (CNN), long short-term memory (LSTM) network and Bi-directional LSTM (Bi-LSTM) network. Simulation results demonstrate the best performa
Anthology ID:
2022.dclrl-1.9
Volume:
Proceedings of the Workshop on Dataset Creation for Lower-Resourced Languages within the 13th Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Editors:
Jonne Sälevä, Constantine Lignos
Venue:
DCLRL
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
68–75
Language:
URL:
https://aclanthology.org/2022.dclrl-1.9
DOI:
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Cite (ACL):
Imane Guellil, Ahsan Adeel, Faical Azouaou, Mohamed Boubred, Yousra Houichi, and Akram Abdelhaq Moumna. 2022. Ara-Women-Hate: An Annotated Corpus Dedicated to Hate Speech Detection against Women in the Arabic Community. In Proceedings of the Workshop on Dataset Creation for Lower-Resourced Languages within the 13th Language Resources and Evaluation Conference, pages 68–75, Marseille, France. European Language Resources Association.
Cite (Informal):
Ara-Women-Hate: An Annotated Corpus Dedicated to Hate Speech Detection against Women in the Arabic Community (Guellil et al., DCLRL 2022)
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PDF:
https://aclanthology.org/2022.dclrl-1.9.pdf