Deep Learning Algorithms for Cyber-Bulling Detection in Social Media Platforms
Mohammed Hussein Obaida, Saleh Mesbah Elkaffas, Shawkat K. Guirguis
Abstract
Social media platforms are among the most widely used means of communication. However, some individuals exploit these platforms for nefarious purposes, with "cyberbullying" being particularly prevalent. Cyberbullying, which involves using electronic means to harass or harm others, is especially common among young people. Consequently, this study aims to propose a model for detecting cyberbullying using a deep learning algorithm. Three datasets from Twitter, Instagram, and Facebook were utilized to predict instances of bullying using the Long Short-Term Memory (LSTM) method. The results obtained revealed the development of an effective model for detecting cyberbullying, addressing challenges faced by previous cyberbullying detection techniques. The model achieved accuracies of approximately 96.64%, 94.49%, and 91.26% for the Twitter, Instagram, and Facebook datasets, respectively.