@inproceedings{kmainasi-etal-2026-aha,
title = "{AHA}-Memes: A Fine-Grained Multimodal Benchmark for Hate Detection in {A}rabic Memes",
author = "Kmainasi, Mohamed Bayan and
Shahroor, Ali Ezzat and
Hasnat, Abul and
Biswas, Md. Rafiul and
Zaghouani, Wajdi and
Alam, Firoj",
editor = "Abdul-Mageed, Muhammad and
Elsayed, Tamer and
Bouamor, Houda and
El-Beltagy, Samhaa and
Al-Khalifa, Hend and
Abdelali, Ahmed and
Elmadany, AbdelRahim and
El Mekki, Abdellah and
Darwish, Kareem and
Alkhereyf, Sakhar and
Luqman, Hamzah and
Khalifa, Salam and
Magdy, Samar and
Alyafeai, Zaid and
Hamed, Injy and
Zaghouani, Wajdi and
Zitouni, Imed",
booktitle = "Proceedings of The Fourth {A}rabic Natural Language Processing Conference",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.arabicnlp-main.27/",
pages = "384--408",
ISBN = "979-8-89176-473-6",
abstract = "Hateful memes are a growing form of multimodal online harm, where hostile intent is often conveyed through the joint interpretation of images, text, cultural references, and implicit targets. While hateful meme detection has advanced in high-resource languages, Arabic remains underexplored, with existing meme resources focusing mainly on propaganda or coarse harmful-content labels. We introduce AHA-Memes, which is, to our knowledge, the first large Arabic hateful meme benchmark with fine-grained, multi-label annotations. The dataset includes 5K manually annotated memes using a taxonomy that captures hate types, i.e., attack strategies. We further provide {\textasciitilde}66K silver-labeled memes to support future studies. We benchmark text-only, image-only, and late-fusion multimodal models, as well as few-shot in-context learning (ICL) and open- and closed-weight Vision-Language Models (VLMs) under zero-shot and fine-tuning settings. Our results establish strong baselines and highlight key challenges in culturally grounded Arabic hateful meme detection. We release the dataset, annotation guidelines, and evaluation scripts to support future research. (**Resources:** https://github.com/MohamedBayan/AHA-Memes) (**Hugging Face:** https://huggingface.co/datasets/QCRI/AHA-MEMES)"
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<abstract>Hateful memes are a growing form of multimodal online harm, where hostile intent is often conveyed through the joint interpretation of images, text, cultural references, and implicit targets. While hateful meme detection has advanced in high-resource languages, Arabic remains underexplored, with existing meme resources focusing mainly on propaganda or coarse harmful-content labels. We introduce AHA-Memes, which is, to our knowledge, the first large Arabic hateful meme benchmark with fine-grained, multi-label annotations. The dataset includes 5K manually annotated memes using a taxonomy that captures hate types, i.e., attack strategies. We further provide ~66K silver-labeled memes to support future studies. We benchmark text-only, image-only, and late-fusion multimodal models, as well as few-shot in-context learning (ICL) and open- and closed-weight Vision-Language Models (VLMs) under zero-shot and fine-tuning settings. Our results establish strong baselines and highlight key challenges in culturally grounded Arabic hateful meme detection. We release the dataset, annotation guidelines, and evaluation scripts to support future research. (**Resources:** https://github.com/MohamedBayan/AHA-Memes) (**Hugging Face:** https://huggingface.co/datasets/QCRI/AHA-MEMES)</abstract>
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%0 Conference Proceedings
%T AHA-Memes: A Fine-Grained Multimodal Benchmark for Hate Detection in Arabic Memes
%A Kmainasi, Mohamed Bayan
%A Shahroor, Ali Ezzat
%A Hasnat, Abul
%A Biswas, Md. Rafiul
%A Zaghouani, Wajdi
%A Alam, Firoj
%Y Abdul-Mageed, Muhammad
%Y Elsayed, Tamer
%Y Bouamor, Houda
%Y El-Beltagy, Samhaa
%Y Al-Khalifa, Hend
%Y Abdelali, Ahmed
%Y Elmadany, AbdelRahim
%Y El Mekki, Abdellah
%Y Darwish, Kareem
%Y Alkhereyf, Sakhar
%Y Luqman, Hamzah
%Y Khalifa, Salam
%Y Magdy, Samar
%Y Alyafeai, Zaid
%Y Hamed, Injy
%Y Zaghouani, Wajdi
%Y Zitouni, Imed
%S Proceedings of The Fourth Arabic Natural Language Processing Conference
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-473-6
%F kmainasi-etal-2026-aha
%X Hateful memes are a growing form of multimodal online harm, where hostile intent is often conveyed through the joint interpretation of images, text, cultural references, and implicit targets. While hateful meme detection has advanced in high-resource languages, Arabic remains underexplored, with existing meme resources focusing mainly on propaganda or coarse harmful-content labels. We introduce AHA-Memes, which is, to our knowledge, the first large Arabic hateful meme benchmark with fine-grained, multi-label annotations. The dataset includes 5K manually annotated memes using a taxonomy that captures hate types, i.e., attack strategies. We further provide ~66K silver-labeled memes to support future studies. We benchmark text-only, image-only, and late-fusion multimodal models, as well as few-shot in-context learning (ICL) and open- and closed-weight Vision-Language Models (VLMs) under zero-shot and fine-tuning settings. Our results establish strong baselines and highlight key challenges in culturally grounded Arabic hateful meme detection. We release the dataset, annotation guidelines, and evaluation scripts to support future research. (**Resources:** https://github.com/MohamedBayan/AHA-Memes) (**Hugging Face:** https://huggingface.co/datasets/QCRI/AHA-MEMES)
%U https://aclanthology.org/2026.arabicnlp-main.27/
%P 384-408
Markdown (Informal)
[AHA-Memes: A Fine-Grained Multimodal Benchmark for Hate Detection in Arabic Memes](https://aclanthology.org/2026.arabicnlp-main.27/) (Kmainasi et al., ArabicNLP 2026)
ACL