Identifying False Content and Hate Speech in Sinhala YouTube Videos by Analyzing the Audio

• URL: http://arxiv.org/abs/2402.01752v1
• Date: Tue, 30 Jan 2024 08:08:34 GMT
• Title: Identifying False Content and Hate Speech in Sinhala YouTube Videos by Analyzing the Audio
• Authors: W. A. K. M. Wickramaarachchi, Sameeri Sathsara Subasinghe, K. K. Rashani Tharushika Wijerathna, A. Sahashra Udani Athukorala, Lakmini Abeywardhana, A. Karunasena
• Abstract summary: This study proposes a solution to minimize the spread of violence and misinformation in Sinhala YouTube videos. The approach involves developing a rating system that assesses whether a video contains false information by comparing the title and description with the audio content.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: YouTube faces a global crisis with the dissemination of false information and hate speech. To counter these issues, YouTube has implemented strict rules against uploading content that includes false information or promotes hate speech. While numerous studies have been conducted to reduce offensive English-language content, there's a significant lack of research on Sinhala content. This study aims to address the aforementioned gap by proposing a solution to minimize the spread of violence and misinformation in Sinhala YouTube videos. The approach involves developing a rating system that assesses whether a video contains false information by comparing the title and description with the audio content and evaluating whether the video includes hate speech. The methodology encompasses several steps, including audio extraction using the Pytube library, audio transcription via the fine-tuned Whisper model, hate speech detection employing the distilroberta-base model and a text classification LSTM model, and text summarization through the fine-tuned BART-Large- XSUM model. Notably, the Whisper model achieved a 48.99\% word error rate, while the distilroberta-base model demonstrated an F1 score of 0.856 and a recall value of 0.861 in comparison to the LSTM model, which exhibited signs of overfitting.

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