Multi-modal Hate Speech Detection using Machine Learning
Fariha Tahosin Boishakhi Ponkoj Chandra Shill Md. Golam Rabiul Alam
With the continuous growth of internet users and media content, it is very
hard to track down hateful speech in audio and video. Converting video or audio
into text does not detect hate speech accurately as human sometimes uses
hateful words as humorous or pleasant in sense and also uses different voice
tones or show different action in the video. The state-ofthe-art hate speech
detection models were mostly developed on a single modality. In this research,
a combined approach of multimodal system has been proposed to detect hate
speech from video contents by extracting feature images, feature values
extracted from the audio, text and used machine learning and Natural language
processing.