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Conference papers

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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Contributor : Ahmed Serhrouchni Connect in order to contact the contributor
Submitted on : Monday, July 19, 2021 - 2:19:41 AM
Last modification on : Wednesday, January 26, 2022 - 2:28:16 PM




Batoul Haidar, Maroun Chamoun, Ahmed Serhrouchni. Arabic Cyberbullying Detection: Using Deep Learning. 2018 7th International Conference on Computer and Communication Engineering (ICCCE), Sep 2018, Kuala Lumpur, Malaysia. pp.284-289, ⟨10.1109/ICCCE.2018.8539303⟩. ⟨hal-03289813⟩



Les métriques sont temporairement indisponibles