标题： 毒标签：利用丰富标签注释解码有毒表情包 显示英文标题

标题： ToxicTAGS: Decoding Toxic Memes with Rich Tag Annotations

摘要： 2025年全球风险报告将基于国家的武装冲突和社会分化列为最紧迫的全球威胁，社交媒体在放大有害言论中起着核心作用。 模因作为一种广泛使用的在线交流方式，经常作为传播有害内容的载体。 然而，数据可及性的限制和数据集整理的高昂成本阻碍了强大模因审核系统的开发。 为了解决这一挑战，本文我们引入了一个首创的数据集，包含6300个真实世界的基于模因的帖子，并进行了两阶段的标注：(i) 有毒和正常内容的二分类，(ii) 对有毒模因进行细粒度标注，分为仇恨、危险或冒犯性。 该数据集的一个关键特点是，它通过社会相关标签的辅助元数据进行了丰富，增强了每个模因的上下文。 此外，我们提出了一种标签生成模块，可以生成基于社会的标签，因为大多数现实中的模因通常不带标签。 实验结果表明，结合这些标签显著提高了最先进的视觉语言模型检测任务的性能。 我们的贡献为多模态在线环境中的内容审核提供了新颖且可扩展的基础。 显示英文摘要

摘要： The 2025 Global Risks Report identifies state-based armed conflict and societal polarisation among the most pressing global threats, with social media playing a central role in amplifying toxic discourse. Memes, as a widely used mode of online communication, often serve as vehicles for spreading harmful content. However, limitations in data accessibility and the high cost of dataset curation hinder the development of robust meme moderation systems. To address this challenge, in this work, we introduce a first-of-its-kind dataset of 6,300 real-world meme-based posts annotated in two stages: (i) binary classification into toxic and normal, and (ii) fine-grained labelling of toxic memes as hateful, dangerous, or offensive. A key feature of this dataset is that it is enriched with auxiliary metadata of socially relevant tags, enhancing the context of each meme. In addition, we propose a tag generation module that produces socially grounded tags, because most in-the-wild memes often do not come with tags. Experimental results show that incorporating these tags substantially enhances the performance of state-of-the-art VLMs detection tasks. Our contributions offer a novel and scalable foundation for improved content moderation in multimodal online environments.