OVALYTICS:增强攻击性视频检测与YouTube转录和先进的语言模型

Sneha Chinivar , Roopa M.S. , Arunalatha J.S. , Venugopal K.R.
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引用次数: 0

摘要

网上令人反感的内容呈指数级增长,这凸显出对内容进行严格监管的必要性。作为回应,本工作提出了OVALYTICS(利用YouTube转录和智能分类系统的攻击性视频分析),这是一个全面的框架,引入了用于攻击性视频检测的先进技术的新集成。与现有方法不同,OVALYTICS独特地将Whisper AI与最先进的大型语言模型(llm)(如BERT, ALBERT, XLM-R, MPNet和T5)相结合,用于准确的音频到文本转录,用于语义分析。该框架还具有为细粒度评估量身定制的新管理数据集,与传统方法相比,在准确性和f1分数方面取得了显着改进,并推进了自动化内容审核的状态。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
OVALYTICS: Enhancing Offensive Video Detection with YouTube Transcriptions and Advanced Language Models
The exponential growth of offensive content online underscores the need for robust content moderation. In response, this work presents OVALYTICS (Offensive Video Analysis Leveraging YouTube Transcriptions with Intelligent Classification System), a comprehensive framework that introduces novel integrations of advanced technologies for offensive video detection. Unlike existing approaches, OVALYTICS uniquely combines Whisper AI for accurate audio-to-text transcription with state-of-the-art large language models (LLMs) such as BERT, ALBERT, XLM-R, MPNet, and T5 for semantic analysis. The framework also features a newly curated dataset tailored for fine-grained evaluation, achieving significant improvements in accuracy and F1-scores over traditional methods and advancing the state of automated content moderation.
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