Exploring social media sentiment patterns for improved cyberbullying detection

Abstract

Cases of online bullying and aggressive behaviors directed at social media users have surged in recent years. These behaviors have had negative impacts on victims from a wide range of demographic groups. While efforts have been made to address persistent digital harassment, the expected outcome has been limited due to the lack of effective tools to quickly identify cyberbullying behaviors and censor them accordingly on social media platforms. This study presents a scalable and systematic method to detect and analyze offensive behavior and bullying on Twitter (now known as X). Our methodology involves extracting textual, user-related, and network-related attributes to understand the traits of individuals involved in such behaviors. This approach aims to recognize distinctive characteristics that set them apart from regular users. This study proposes a novel model by employing an integrated deep-learning model, combining the bidirectional gated recurrent unit (BiGRU), transformer block, and convolutional neural network (CNN). This model aims to classify X comments into offensive and non-offensive categories. The proposed model’s efficiacy has been evaluated through several experiments by combining three widely recognized datasets of hate speech. The proposed model achieves an accuracy rate of approximately 98.95%, showing promising results in identifying and categorizing offensive behavior in cyberbullying.