From the 1 of 6 linked papers with an AI index.
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AHA-Memes: A Fine-Grained Multimodal Benchmark for Understanding Hate in Arabic Memes
Mohamed Bayan Kmainasi, Ali Ezzat Shahroor, Abul Hasnat +3
The paper presents AHA-Memes, a large-scale Arabic hateful meme dataset with fine-grained, multi‑label annotations, and provides baseline evaluations of text, image, and multimodal…
Adapting Reinforcement Learning with Chain-of-Thought Supervision for Explainable Detection of Hateful and Propagandistic Memes
Mohamed Bayan Kmainasi, Mucahid Kutlu, Ali Ezzat Shahroor +2
Hateful and propagandistic memes exploit the interplay between images and text to convey harmful intent that neither modality reveals alone. Although thinking-based multimodal larg…
Can Thinking Models Think to Detect Hateful Memes?
Mohamed Bayan Kmainasi, Mucahid Kutlu, Ali Ezzat Shahroor +2
Hateful memes often require compositional multimodal reasoning: the image and text may appear benign in isolation, yet their interaction conveys harmful intent. Although thinking-b…
MemeIntel: Explainable Detection of Propagandistic and Hateful Memes
Mohamed Bayan Kmainasi, Abul Hasnat, Md Arid Hasan +2
The proliferation of multimodal content on social media presents significant challenges in understanding and moderating complex, context-dependent issues such as misinformation, ha…
ArMeme: Propagandistic Content in Arabic Memes
Firoj Alam, Abul Hasnat, Fatema Ahmed +2
With the rise of digital communication, memes have become a significant medium for cultural and political expression that is often used to mislead audiences. Identification of such…