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 what makes a meme hateful. Drawing on insights from philosophy and psychology, we argue that hateful memes are characterized by two essential features: a presupposed context and the expression of false claims. To capture presupposed context, we develop PCM for modeling contextual information across modalities. To detect false claims, we introduce the FACT module, which integrates external knowledge and harnesses cross-modal reference graphs.

BibTeX Copy

@inproceedings{cai2025unpackinghateful,
  title = {Unpacking Hateful Memes: Presupposed Context and False Claims},
  author = {Weibin Cai and Jiayu Li and Reza Zafarani},
  year = {2026},
  keywords = {conference},
  booktitle = {Proceedings of the 2026 SIAM International Conference on Data Mining (SDM)},
  address = {Salt Lake City, USA},
  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 what makes a meme hateful. Drawing on insights from philosophy and psychology, we argue that hateful memes are characterized by two essential features: a presupposed context and the expression of false claims. To capture presupposed context, we develop PCM for modeling contextual information across modalities. To detect false claims, we introduce the FACT module, which integrates external knowledge and harnesses cross-modal reference graphs.},
  url = {https://arxiv.org/abs/2510.09935},
  pdf = {https://arxiv.org/pdf/2510.09935},
}