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English(EN) Harmful YouTube Video Detection: A Taxonomy of Online Harm and MLLMs as Alternative Annotators

多模态大语言模型被测试为有害YouTube视频的标注者,并引入新的危害分类法

研究人员开发了一个全面的在线危害分类法,用于视频平台上的在线危害,将其分为六种类型:信息、仇恨与骚扰、成瘾性、诱导点击、性以及身体伤害。他们还探索了使用多模态大语言模型(MLLMs),特别是GPT-4 Turbo,作为有害视频的替代标注者。使用MLLMs、众包工作者和训练有素的标注者对19,422个YouTube视频的数据集进行了标注,以评估MLLMs在内容审核和研究中的有效性。 AI

影响 MLLMs在视频平台的内容审核和社科研究方面展现出作为经济高效替代方案的潜力。

排序理由 学术论文,详细介绍了有害内容检测的新分类法和方法论。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

多模态大语言模型被测试为有害YouTube视频的标注者,并引入新的危害分类法

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Signal score
23 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Tool
学术论文,详细介绍了有害内容检测的新分类法和方法论。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration
Single-source cluster
Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
paper, safety
Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance
High
Clearly on-topic for AI-industry coverage.
Story freshness
Breaking (< 6h)
Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

完整方法见我们的编辑标准。

报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Claire Wonjeong Jo, Miki Weso{\l}owska, Magdalena Wojcieszak ·

    有害YouTube视频检测:在线危害分类及MLLMs作为替代标注者

    arXiv:2411.05854v2 Announce Type: replace-cross Abstract: Video platforms, used by billions globally, are criticized for exposing users to harmful content. Yet, detecting online harm remains challenging due to inconsistent approaches to what constitutes harm and limitations of hu…