Detection of Hate Speech in Videos Using Machine Learning
Ching Seh Wu, Unnathi Bhandary
Abstract
With the progression of the Internet and social media, people are given multiple platforms to share their thoughts and opinions about various subject matters freely. However, this freedom of speech is misused to direct hate towards individuals or group of people due to their race, religion, gender etc. The rise of hate speech has led to conflicts and cases of cyber bullying, causing many organizations to look for optimal solutions to solve this problem. Developments in the field of machine learning and deep learning have piqued the interest of researchers, leading them to research and implement solutions to solve the problem of hate speech. Currently, machine learning techniques are applied to textual data to detect hate speech. With the ample use of video sharing sites, there is a need to find a way to detect hate speech in videos. This research deals with classification of videos into normal or hateful categories based on the spoken content of the videos. The video dataset is built using a crawler to search and download videos based on offensive words that are specified as keywords. The audio is extracted from the videos and is converted into textual format using a Speech-to-Text converter to obtain a transcript of the videos. Experiments are conducted by training four models with three different feature sets extracted from the dataset. The models are evaluated by computing the specified evaluation metrics. The evaluated metrics indicate that Random Forrest Classifier model delivers the best results in classifying videos.