Unpacking Hateful Memes: Presupposed Context and False Claims
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866911203763159040 |
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| author | Cai, Weibin Li, Jiayu Zafarani, Reza |
| author_facet | Cai, Weibin Li, Jiayu Zafarani, Reza |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_09935 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unpacking Hateful Memes: Presupposed Context and False Claims Cai, Weibin Li, Jiayu Zafarani, Reza Computation and Language Artificial Intelligence 68T50, 68T45, 68T07 I.2.7; I.2.10; I.2.6 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. |
| title | Unpacking Hateful Memes: Presupposed Context and False Claims |
| topic | Computation and Language Artificial Intelligence 68T50, 68T45, 68T07 I.2.7; I.2.10; I.2.6 |
| url | https://arxiv.org/abs/2510.09935 |