Hate Speech Detection in Social Media Using BiLSTM

Social media platforms also function as breeding grounds for hate speech. Hate speech is offensive language targeting people based on their characteristics, including gender, race, or religion. The existing hate speech detection system relies on sentiment analysis, which is limited to processing static data that has been extracted from social media platforms beforehand. This research focuses on the development of a hate speech detection system specifically designed for social media platforms. By utilizing Bidirectional Long Short-Term Memory (BiLSTM) networks, the proposed system seeks to precisely detect and categorize hate speech in real time, allowing for rapid mitigation and intervention measures. The network can grasp both past and future context by processing text inputs bidirectionally, enabling a thorough comprehension of semantic linkages and linguistic nuances. The system integrates a chat evaluator module to monitor real-time conversations, issuing warnings and blocking users based on a threshold of detected hate speech occurrences. The implementation of a bidirectional strategy improves the model's overall accuracy and decreases false positives by enhancing its capacity to identify minute fluctuations in hate speech patterns. This initiative contributes to the continuous endeavors aimed at combating hate speech, thereby fostering a digital environment that is more inclusive and secure.