Hateful tweet detection using a BiLSTM-BiGRU: An ensemble perspective

Social media hate speech is an emerging issue, and there is a need to create automatic systems to identify and mitigate its effects. The rapid expansion of social media platforms, especially Twitter, has facilitated the dissemination of hate speech, presenting a major challenge for online communities. Such speech can have severe social and psychological consequences, including inciting violence, promoting extremism, and affecting mental health. Thus, it is essential to manage hateful content on Twitter. This paper presents an ensemble deep learning model that combines BiLSTM and BiGRU to enhance prediction accuracy and robustness. The model achieved 98.56% accuracy rate and demonstrated better generalization than existing methods, proving its effectiveness in identifying hate speech with fewer false positives. This paper offers a powerful tool for detecting and preventing harmful online behavior, contributing to a safer and more inclusive digital space.