We are pleased to announce MAHED 2025, the first multimodal shared task dedicated to Hope and Hate Detection in Arabic content. This novel multimodal challenge will be co-located with EMNLP 2025 at the ArabicNLP 2025 Conference.
MAHED 2025 addresses critical real-world challenges in Arabic natural language processing by focusing on the detection of hate speech, hope speech, and emotions in both Arabic text and memes. This shared task aims to advance research in ethical AI while addressing the linguistic diversity and dialectal variations inherent in Arabic content. The shared task comprises three subtasks: Task 1: Text-based Hope & Hate Speech Classification Participants will develop models to classify Arabic text as containing hope speech, hate speech, or neutral content. Task 2: Multitask Learning for Emotion, Offensive Content, and Hate Detection This task involves simultaneous detection of emotions, offensive language, and hate speech in Arabic text. Task 3: Multimodal Hateful Meme Detection Participants will work with Arabic memes to detect hateful content using both textual and visual modalities. Registration Links: * Task 1: https://www.codabench.org/competitions/9136/ * Task 2: https://www.codabench.org/competitions/9166/ * Task 3: https://www.codabench.org/competitions/9192/ Important Dates: * June 10, 2025: Training data and evaluation scripts released * July 20, 2025: Final registration deadline and test set release * July 25, 2025: Test submission deadline * November 5-9, 2025: ArabicNLP 2025 Workshop at EMNLP 2025, Suzhou, China Resources and Registration: Website: https://marsadlab.github.io/mahed2025/ Dataset and Code: https://github.com/marsadlab/MAHED2025Dataset
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