Unpacking Hateful Memes: Presupposed Context and False Claims

• URL: http://arxiv.org/abs/2510.09935v1
• Date: Sat, 11 Oct 2025 00:25:27 GMT
• Title: Unpacking Hateful Memes: Presupposed Context and False Claims
• Authors: Weibin Cai, Jiayu Li, Reza Zafarani,
• Abstract summary: hateful meme detection mainly rely on pre-trained language models.<n>We argue that hateful memes are characterized by two essential features: a textbfpresupposed context and the expression of textbffalse claims<n>We introduce textbftextsfSHIELD, a hateful meme detection framework designed to capture the fundamental nature of hate.
• Score: 4.251395949891149
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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