Cyberbullying detection system focusing on the isiXhosa language
Vuyokazi Matomela, Andre Henney · 2022
Due to the Covid19 pandemic, and the restrictions placed in social interactions, there has been an upsurge in the use of social networks, such as Facebook, WhatsApp, Instagram, Telegram, Twitter, and others. As more people turn to the social networks for social interaction, there has been increased occurrences of cyberbullying. Cyberbullying is a type of bullying that occurs through online technology, whereby harmful texts and pictures are shared through social networks. This research project aimed to develop a system that can detect cyberbullying on social networks such as Twitter focusing on the IsiXhosa language. Machine learning algorithms were applied to Twitter feeds in order to detect cyberbullying. The project will help law enforcement to apprehend and prosecute cyberbullies that make threats using isiXhosa. The methodology used incorporated machine learning algorithms to fully implement the cyberbullying detection system. It starts with collecting the data from Twitter using Python, cleaning the data followed by testing the data. The results show that the implementation successfully collected the desired data from Twitter and the data was then pre-processed and prepared to be tested using the different algorithms mentioned in the paper.