{"ID":2856938,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.09935","arxiv_id":"2510.09935","title":"Unpacking Hateful Memes: Presupposed Context and False Claims","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.","short_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 ph...","url_abs":"https://arxiv.org/abs/2510.09935","url_pdf":"https://arxiv.org/pdf/2510.09935v1","authors":"[\"Weibin Cai\",\"Jiayu Li\",\"Reza Zafarani\"]","published":"2025-10-11T00:25:27Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.AI\"]","methods":"[\"Language Model\"]","has_code":false}