Abstract: With the exponential increase of social media users, cyberbullying has been emerged as a form of bullying through electronic messages. Social networks provides a rich environment for bullies to uses these networks as vulnerable to attacks against victims. Given the consequences of cyberbullying on victims, it is necessary to find suitable actions to detect and prevent it. Machine learning can be helpful to detect language patterns of the bullies and hence can generate a model to automatically detect cyberbullying actions. This paper proposes a supervised machine learning approach for detecting and preventing cyberbullying. Several classifiers are used to train and recognize bullying actions. The evaluation of the proposed approach on cyberbullying dataset shows that Neural Network performs better and achieves accuracy of 92.8% and SVM achieves 90.3. Also, NN outperforms other classifiers of similar work on the same dataset.

John Hani, Mohamed Nashaat, Mostafa Ahmed, Zeyad Emad, Eslam Amer and Ammar Mohammed, “Social Media Cyberbullying Detection using Machine Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 10(5), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100587

@article{Hani2019,
title = {Social Media Cyberbullying Detection using Machine Learning},
journal = {International Journal of Advanced Computer Science and Applications}
doi = {10.14569/IJACSA.2019.0100587},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100587},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {5},
author = {John Hani and Mohamed Nashaat and Mostafa Ahmed and Zeyad Emad and Eslam Amer and Ammar Mohammed},
}