Abstract
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.
Abstract (translated)
仇恨言论是现代社会的一个紧迫问题，对在线和离线领域都产生了重大影响。最近，仇恨言论检测的研究主要集中在基于文本的媒体上，很大程度上忽略了多模态内容，如视频。现有的仇恨视频数据集研究主要集中在西方语境下的英语内容，并局限于二元标签（仇恨或非仇恨），缺乏详细的上下文信息。本研究提出了MultiHateClip1，一个由仇恨词汇和人类标注创建的多语言数据集。它旨在提高YouTube和Bilibili等平台上的仇恨视频检测，包括英语和中文内容。这个数据集包括2000个被标注为仇恨性、攻击性和正常性的视频，为基于性别仇恨言论提供了一个跨文化视角。通过详细审查人类标注结果，我们讨论了中文和英文仇恨视频之间的差异，并强调了两类视频分析中不同模态的重要性。评估了最先进的视频分类模型，如VLM、GPT-4V和Qwen-VL，在MultiHateClip上的效果，突出了准确区分仇恨和攻击性内容以及迫切需要具有多模态和文化细微的模型。MultiHateClip在增强仇恨视频检测方面取得了基础性的进展，强调在打击网络仇恨言论方面需要多模态和敏感的方法。
URL
https://arxiv.org/abs/2408.03468
