Detection of Arabic Cyberbullying on Social Networks using Machine Learning
Djedjiga Mouheb, Raghad Albarghash, Mohamad Fouzi Mowakeh, Zaher Al Aghbari, Ibrahim Kamel · 2019
Recently, cyberbullying has grown significantly on social platforms, impacting users especially teenagers and young adults. The effects of this threat are so severe and damaging that could lead to suicide. Lately, this threat has become a significant issue in Arab countries, especially with the wide adoption of social media by the young generation. Most of existing research proposed solutions for detecting cyberbullying, mainly in English language. However, only few papers studied cyberbullying detection in Arabic Social Media Communications. This paper used machine learning for automatic detection of cyberbullying in Arabic. The proposed scheme detects cyberbullying using Naive Bayes(NB) classifier algorithm by training and testing the classifier with real data set which was collected from Youtube and Twitter.