Arabic Cyberbullying Detection: Using Deep Learning
Abstract
As much as internet and smart devices are taking a big role in the lives of children and adolescents, also the threat of Cyberbullying on the lives and wellbeing of those youngsters is rising. The threat of cyberbullying is acknowledged around the world generally and in the Arabic areas specifically. A lot of research is done for finding automated solutions for cyberbullying detection in several languages, but not much has been done for Arabic Language. At the other hand, a lot of interest is invested in Deep Learning techniques, where Deep Learning has been applied in several areas and showed vast success. Thus this paper proposes a solution that employs Deep Learning methods in the process of Arabic Cyberbullying Detection. Specifically a Feed Forward Neural Network is trained on an Arabic Dataset for the purpose of cyberbullying detection.
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One-hot and TF-IDF approaches are utilized in a few studies in cyberbullying identification, but these techniques perform weaker since they tend to be context insensitive.
“One-hot [74] and TF-IDF approaches are utilized in a few studies in the identification of cyberbullying. However, these techniques perform weaker since they tend to be context insensitive.”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [74]
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References 28 · Cited by 58
- Authors
- Haidar, B., Chamoun, M., & Serhrouchni, A.
- Affiliations
- Saint Joseph University (LB)
- Télécom Paris (FR)
- ORCID
- Chamoun, Maroun
- Venue
- 2018 7th International Conference on Computer and Communication Engineering (ICCCE)
- Publisher
- IEEE
- Published
- 2018-09
- Pages
- 284-289
- OpenAlex
- W2900491514
- Cited by
- 58
- References
- 28
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 14 · 2023: 13 · 2022: 9 · 2021: 8 · 2020: 5 · 2019: 4
- FWCI
- 1.73
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Computer and Communication Engineering (publishedVersion)
- HAL (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Keywords
- cyberbullying detection, Arabic language, deep learning, online abuse
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