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- W4285195554 abstract "In recent years, the number of online social networks users is dramatically increased. Cyberbullying is a serious threat in online social networks especially toward children and teenagers. Victims are harassed by perpetrators even with no physical attendance. Cyberbullying causes psychological damage to victims resulting in anxiety, depression, and even suicide. Cyberbullying is language and culture sensitive. Many prior works in cyberbullying detection was conducted in English and only few papers studied Arabic cyberbullying detection. In this research, Arabic cyberbullying detection using five machine learning techniques were studied using real Arabic messages from Twitter and YouTube. Many performance techniques were used to evaluate the robustness of the model. Naïve Bayes (NB) achieves the highest Receiver Operating Characteristic (ROC) curve (AUC) of 89% while Support Vector Machine (SVM) and Logistic Regression (LR) both achieve 88%." @default.
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- W4285195554 date "2022-01-01" @default.
- W4285195554 modified "2023-09-25" @default.
- W4285195554 title "Arabic Cyberbullying Detection from Imbalanced Dataset Using Machine Learning" @default.
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- W4285195554 doi "https://doi.org/10.1007/978-3-031-05767-0_31" @default.
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