解析仇恨表情包：预设语境与虚假主张
Unpacking Hateful Memes: Presupposed Context and False Claims
AI总结：
本文针对仇恨表情包检测中忽略仇恨本质的问题，提出含PCM与FACT模块的SHIELD框架，实验显示其检测性能优于现有最优方法，且可用于假新闻检测等任务。
摘要：
表情包虽常具幽默感，但也常被用于传播仇恨，对个人与社会造成严重危害。当前仇恨表情包检测方法主要依赖预训练语言模型，却较少关注“表情包为何具有仇恨性”这一核心问题。本文借鉴哲学与心理学的见解，指出仇恨表情包具备两个核心特征：预设语境与虚假主张的表达。为捕捉预设语境，我们开发了PCM（Presupposed Context Model，预设语境模型）以建模跨模态语境信息；为检测虚假主张，我们引入FACT模块，该模块整合外部知识并利用跨模态参考图。结合PCM与FACT，我们提出仇恨表情包检测框架SHIELD，旨在捕捉仇恨的本质。大量实验表明，SHIELD在各数据集与指标上均优于现有最优方法，且在假新闻检测等其他任务上展现出通用性。
英文摘要：
While memes are often humorous, they are frequently used to disseminate hate, causing serious harm to individuals and society. Current approaches to hateful meme detection mainly rely on pre-trained language models. However, less focus has been dedicated to \textit{what make a meme hateful}. Drawing on insights from philosophy and psychology, we argue that hateful memes are characterized by two essential features: a \textbf{presupposed context} and the expression of \textbf{false claims}. To capture presupposed context, we develop \textbf{PCM} for modeling contextual information across modalities. To detect false claims, we introduce the \textbf{FACT} module, which integrates external knowledge and harnesses cross-modal reference graphs. By combining PCM and FACT, we introduce \textbf{\textsf{SHIELD}}, a hateful meme detection framework designed to capture the fundamental nature of hate. Extensive experiments show that SHIELD outperforms state-of-the-art methods across datasets and metrics, while demonstrating versatility on other tasks, such as fake news detection.
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。
