MultiHateClip: 面向YouTube与Bilibili仇恨视频检测的多语言基准数据集
MultiHateClip: A Multilingual Benchmark Dataset for Hateful Video Detection on YouTube and Bilibili
AI总结：
本研究构建了覆盖中英双语、含2000条视频的多语言仇恨视频检测基准数据集MultiHateClip，分析了跨文化仇恨视频差异，评估发现现有前沿模型在区分仇恨与冒犯内容上存在不足，为相关研究提供了基础支撑。
摘要：
仇恨言论是现代社会的一个紧迫问题，在线上和线下都产生了重大影响。当前仇恨言论检测研究主要集中在基于文本的媒体上，很大程度上忽视了视频等多模态内容。现有的仇恨视频数据集研究主要聚焦西方语境下的英文内容，且仅限于二分类标签（仇恨或非仇恨），缺乏详细的上下文信息。本研究提出了MultiHateClip，这是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YouTube、Bilibili等平台上的仇恨视频检测能力，覆盖英文和中文两种语言的内容。该数据集包含2000条经过标注的视频，标注类别为仇恨性、冒犯性和正常性，为基于性别的仇恨言论研究提供了跨文化视角。通过对人工标注结果的详细分析，我们探讨了中英文仇恨视频之间的差异，并强调了不同模态在仇恨与冒犯性视频分析中的重要性。在MultiHateClip上对VLM、GPT-4V、Qwen-VL等前沿视频分类模型的评估显示，当前模型在准确区分仇恨与冒犯性内容方面仍存在挑战，迫切需要兼具多模态能力与文化敏感度的模型。MultiHateClip通过强调多模态、文化敏感的方法在打击网络仇恨言论中的必要性，为提升仇恨视频检测能力奠定了基础性进展。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
