Behaviour Analysis Among Adolescents And Children For Cyberbullying Based On Twitter And Kaggle Dataset - Afiefah Hannani, Abdul Halim (2023)
Nowadays, the online platform is primarily used to communicate and connect with people. It controlled many lives based on many aspects. When online, not all users prefer what they see. They can speak up without thinking about the effect on someone. From this event, it may lead to cyberbullying activity since all the users are free to throw their opinion on social media without hesitation. Not only that, but based on online judgment, it can also affect someone’s mental health. Therefore, cyberbullying detection using a Machine Learning approach is suggested. This study presents the comparison of three Machine Learning algorithms for the detection using cyberbullying activity on social media platforms specifically Twitter. The dataset to perform the algorithm will be retrieved from an open-source website called Kaggle where it will be used for the training and testing process. The algorithms include Support Vector Machine (SVM), Naïve Bayes, and Decision Tree. The purpose of this study is to see the accuracy of the algorithms and compare it. The highest algorithm will be chosen as the best model and algorithm that can be used to detect cyberbullying tweet text.