Wassen Aldjanabi
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Wassen Aldjanabi has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Abdelghani Dahou
Zhejiang Normal University
1 shared publication - Ahmed Mohamed Helmi
Department of Computer and Systems Engineering, Faculty of Engineering, Zagazig University, Zagazig, Egypt
1 shared publication - Mohamed Abd Elaziz
Zagazig University
1 shared publication - Mohammed Abdulaziz Aide Al-Qaness
College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua 321004, China
1 shared publication - Robertas Damaševičius
Kaunas University of Technology
1 shared publication
Latest Publications
- Open Access
- 8 October 2021
- Published by MDPI AG in Informatics
- Vol. 8 (4) , 69
Arabic Offensive and Hate Speech Detection Using a Cross-Corpora Multi-Task Learning Model
As social media platforms offer a medium for opinion expression, social phenomena such as hatred, offensive language, racism, and all forms of verbal violence have increased spectacularly. These behaviors do not affect specific countries, groups, or communities only, extending beyond these areas into people’s everyday lives. This study investigates offensive and hate speech on Arab social media to build an accurate offensive and hate speech detection system. More precisely, we develop a classification system for determining offensive and hate speech using a multi-task learning (MTL) model built on top of a pre-trained Arabic language model. We train the MTL model on the same task using cross-corpora representing a variation in the offensive and hate context to learn global and dataset-specific contextual representations. The developed MTL model showed a significant performance and outperformed existing models in the literature on three out of four datasets for Arabic offensive and hate speech detection tasks.
- 8 October 2021
- Published by MDPI AG in Informatics
- Vol. 8 (4) , 69
Open AccessArabic Offensive and Hate Speech Detection Using a Cross-Corpora Multi-Task Learning Model
As social media platforms offer a medium for opinion expression, social phenomena such as hatred, offensive language, racism, and all forms of verbal violence have increased spectacularly. These behaviors do not affect specific countries, groups, or communities only, extending beyond these areas into people’s everyday lives. This study investigates offensive and hate speech on Arab social media to build an accurate offensive and hate speech detection system. More precisely, we develop a classification system for determining offensive and hate speech using a multi-task learning (MTL) model built on top of a pre-trained Arabic language model. We train the MTL model on the same task using cross-corpora representing a variation in the offensive and hate context to learn global and dataset-specific contextual representations. The developed MTL model showed a significant performance and outperformed existing models in the literature on three out of four datasets for Arabic offensive and hate speech detection tasks.