Abstract: The widespread use of computers and smartphones has led to an increase in social media usage, where users can express their opinions freely. However, this freedom of expression can be misused for spreading abusive and bullying content online. To ensure a safe online environment, cybersecurity experts are continuously researching effective and intelligent ways to respond to such activities. In this work, we present ArCyb, a robust machine-learning model for detecting cyberbullying in social media using a manually labeled Arabic dataset. The model achieved 89% prediction accuracy, surpassing the state-of-the-art cyberbullying models. The results of this work can be utilized by social media platforms, government agencies, and internet service providers to detect and prevent the spread of bullying posts in social networks.

Khalid T. Mursi, Abdulrahman Y. Almalki, Moayad M. Alshangiti, Faisal S. Alsubaei and Ahmed A. Alghamdi. “ArCyb: A Robust Machine-Learning Model for Arabic Cyberbullying Tweets in Saudi Arabia”. International Journal of Advanced Computer Science and Applications (IJACSA) 14.9 (2023). http://dx.doi.org/10.14569/IJACSA.2023.01409110

@article{Mursi2023,
title = {ArCyb: A Robust Machine-Learning Model for Arabic Cyberbullying Tweets in Saudi Arabia},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.01409110},
url = {http://dx.doi.org/10.14569/IJACSA.2023.01409110},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {9},
author = {Khalid T. Mursi and Abdulrahman Y. Almalki and Moayad M. Alshangiti and Faisal S. Alsubaei and Ahmed A. Alghamdi}
}