aiXplain at Arabic Hate Speech 2022: An Ensemble Based Approach to Detecting Offensive Tweets

Salaheddin Alzubi, Thiago Castro Ferreira, Lucas Pavanelli, Mohamed Al-Badrashiny

Abstract
Abusive speech on online platforms has a detrimental effect on users’ mental health. This warrants the need for innovative solutions that automatically moderate content, especially on online platforms such as Twitter where a user’s anonymity is loosely controlled. This paper outlines aiXplain Inc.’s ensemble based approach to detecting offensive speech in the Arabic language based on OSACT5’s shared sub-task A. Additionally, this paper highlights multiple challenges that may hinder progress on detecting abusive speech and provides potential avenues and techniques that may lead to significant progress.
Anthology ID:
2022.osact-1.28
Volume:
Proceedinsg of the 5th Workshop on Open-Source Arabic Corpora and Processing Tools with Shared Tasks on Qur'an QA and Fine-Grained Hate Speech Detection
Month:
June
Year:
2022
Address:
Marseille, France
Editors:
Hend Al-Khalifa, Tamer Elsayed, Hamdy Mubarak, Abdulmohsen Al-Thubaity, Walid Magdy, Kareem Darwish
Venue:
OSACT
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
214–217
Language:
URL:
https://aclanthology.org/2022.osact-1.28
DOI:
Bibkey:
Cite (ACL):
Salaheddin Alzubi, Thiago Castro Ferreira, Lucas Pavanelli, and Mohamed Al-Badrashiny. 2022. aiXplain at Arabic Hate Speech 2022: An Ensemble Based Approach to Detecting Offensive Tweets. In Proceedinsg of the 5th Workshop on Open-Source Arabic Corpora and Processing Tools with Shared Tasks on Qur'an QA and Fine-Grained Hate Speech Detection, pages 214–217, Marseille, France. European Language Resources Association.
Cite (Informal):
aiXplain at Arabic Hate Speech 2022: An Ensemble Based Approach to Detecting Offensive Tweets (Alzubi et al., OSACT 2022)
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PDF:
https://preview.aclanthology.org/nschneid-patch-1/2022.osact-1.28.pdf
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