Real-Time Amharic Hate Speech Detection in Live Streams and Video Chats

This work proposes a novel approach to real-time hate speech detection in Amharic live streams and video chats using state-of-the-art deep learning techniques. The extensive use of social media and live communication tools has amplified hateful and offensive content, and thus it is essential to develop efficient mitigation strategies. The system uses multimodal data, including text, audio, and video, to efficiently detect and mitigate hate speech. By integrating multiple sources of data, the system provides a general overview of the content, identifying both implicit and explicit hate speech. The text analysis module employs a Bidirectional Long Short-Term Memory (BiLSTM) network to process chat messages and comments, while the audio analysis module employs Convolutional Neural Networks (CNN) to extract and process acoustic features. The computer vision module of video analysis detects visual cues of hate speech. Integration of the modalities enables the system to output robust and reliable results. Experimental results confirm the effectiveness of the proposed system for various real-world applications, including live streaming sessions and interactive video conversations. The system achieved high accuracy, precision, recall, and F1-score, proving its applicability for real-time deployment. This paper contributes to the corpus of hate speech detection and provides valuable insights for developing similar systems for other low-resource languages.