Cyberbullying Detection on Social Media Platforms Utilizing Different Machine Learning Approaches

Cyberbullying in social media significantly impacts mental well-being of individuals and poses noteworthy barriers to creating safe online environments, especially in non-English speaking communities.Addressing cyberbullying challenges requires collaborative efforts from communities, educators, and technology platforms developers or designers.The primary concern of this study is to detect cyberbullying in Bangla language, utilizing various machine learning (ML) approaches.A cyberbullying Bangla dataset encompasses a range of texts, including both cyberbullying and non-cyberbullying content.This dataset undergoes preprocessing stage, whilst utilizing diverse techniques, including tokenization, data augmentation, and transformation into sequences, for facilitating the creation of appropriate inputs for various ML approaches such as XGBoost (XGB), Gradient Boosting (GB), Decision Tree (DT), Random Forest (RF), Artificial Neural Network (ANN), Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM).Data is collected using web scraping from different social media platforms, which contains five distinct categories: neutral, threat, troll, political and sexual categories.Experimental results indicated that the proposed cyberbullying detection model achieves an exceptional accuracy of 99.80% with LSTM, surpassing other deep learning based algorithms.Conversely, XGB achieves a commendable accuracy of over 74% with the same dataset, outperforming other traditional ML algorithms.The findings contribute significantly to the development of proactive measures to prevent and mitigate cyberbullying, eventually advancing a safer online environment for individuals communicating in Bangla.