arXiv (All)
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Harmful YouTube Video Detection: A Taxonomy of Online Harm and MLLMs as Alternative Annotators
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 human annotation. We address both challenges. Theoretically, we integrate and ex
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