Hate speech is a pressing issue in modern society, with significant effects both online and offline. Recent research in hate speech detection has primarily centered on text-based media, largely overlooking multimodal content such as videos. Existing studies on hateful video datasets have predominantly focused on English content within a Western context and have been limited to binary labels (hateful or non-hateful), lacking detailed contextual information. This study presents MultiHateClip1 , an novel multilingual dataset created through hate lexicons and human annotation. It aims to enhance the detection of hateful videos on platforms such as YouTube and Bilibili, including content in both English and Chinese languages. Comprising 2,000 videos annotated for hatefulness, offensiveness, and normalcy, this dataset provides a cross-cultural perspective on gender-based hate speech. Through a detailed examination of human annotation results, we discuss the differences between Chinese and English hateful videos and underscore the importance of different modalities in hateful and offensive video analysis. Evaluations of state-of-the-art video classification models, such as VLM, GPT-4V and Qwen-VL, on MultiHateClip highlight the existing challenges in accurately distinguishing between hateful and offensive content and the urgent need for models that are both multimodally and culturally nuanced. MultiHateClip represents a foundational advance in enhancing hateful video detection by underscoring the necessity of a multimodal and culturally sensitive approach in combating online hate speech.

翻译：仇恨言论是现代社会亟待解决的严峻问题，对线上与线下环境均产生显著影响。当前仇恨言论检测研究主要集中于文本媒体，很大程度上忽视了视频等多模态内容。现有关于仇恨视频数据集的研究大多聚焦于西方语境下的英文内容，且仅限于二元标签（仇恨性或非仇恨性），缺乏详细的上下文信息。本研究提出MultiHateClip——一个通过仇恨词典与人工标注构建的新型多语言数据集，旨在提升对YouTube及Bilibili等平台中（包含英文与中文内容的）仇恨视频的检测能力。该数据集包含2000个标注了仇恨性、冒犯性与正常性标签的视频，为基于性别的仇恨言论提供了跨文化视角。通过对人工标注结果的细致分析，我们探讨了中英文仇恨视频的差异，并强调了多模态信息在仇恨性与冒犯性视频分析中的重要性。基于VLM、GPT-4V及Qwen-VL等前沿视频分类模型在MultiHateClip上的评估结果表明：当前模型在准确区分仇恨内容与冒犯内容方面仍面临挑战，亟需开发兼具多模态感知与文化敏感性的模型。MultiHateClip通过强调采用多模态与文化敏感方法应对网络仇恨言论的必要性，为提升仇恨视频检测能力奠定了重要基础。