@inproceedings{kmainasi-etal-2025-memeintel,
    title = "{M}eme{I}ntel: Explainable Detection of Propagandistic and Hateful Memes",
    author = "Kmainasi, Mohamed Bayan  and
      Hasnat, Abul  and
      Hasan, Md Arid  and
      Shahroor, Ali Ezzat  and
      Alam, Firoj",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1539/",
    pages = "30251--30267",
    ISBN = "979-8-89176-332-6",
    abstract = "The proliferation of multimodal content on social media presents significant challenges in understanding and moderating complex, context-dependent issues such as misinformation, hate speech, and propaganda. While efforts have been made to develop resources and propose new methods for automatic detection, limited attention has been given to label detection and the generation of explanation-based rationales for predicted labels. To address this challenge, we introduce MemeXplain, an explanation-enhanced dataset for propaganda memes in Arabic and hateful memes in English, making it the first large-scale resource for these tasks. To solve these tasks, we propose a novel multi-stage optimization approach and train Vision-Language Models (VLMs). Our results demonstrate that this approach significantly improves performance over the base model for both label detection and explanation generation, outperforming the current state-of-the-art with an absolute improvement of approximately 3{\%} on ArMeme and 7{\%} on Hateful Memes. For reproducibility and future research, we aim to make the MemeXplain dataset and scripts publicly available."
}