Cyberbullying-related Hate Speech Detection Using Shallow-to-deep Learning

Communication in society had developed within cultural and geographical boundaries prior to the invention of digital technology. The latest advancements communication technology have significantly surpassed conventional constraints for with regards time location. These new platforms ushered a age user-generated content, online chats, social network comprehensive data on individual behavior. However, abuse software such as media websites, communities, chats has resulted kind hostility aggressive actions. Due widespread use networking technological gadgets, bullying migrated from physical form online, where it is termed Cyberbullying. recently technologies machine learning deep been showing their efficiency identifying linguistic patterns used by cyberbullies cyberbullying detection problem. In this research paper, we aimed evaluate shallow methods We deployed three six algorithms problems. results show that bidirectional long-short-term memory most efficient method detection, terms accuracy recall.