@inproceedings{mia-fahim-2025-banhateme,
    title = "{B}an{H}ate{ME} : Understanding Hate in {B}angla Memes thorough Detection, Categorization, and Target Profiling",
    author = "Mia, Md Ayon  and
      Fahim, Md",
    editor = "Alam, Firoj  and
      Kar, Sudipta  and
      Chowdhury, Shammur Absar  and
      Hassan, Naeemul  and
      Prince, Enamul Hoque  and
      Tasnim, Mohiuddin  and
      Rony, Md Rashad Al Hasan  and
      Rahman, Md Tahmid Rahman",
    booktitle = "Proceedings of the Second Workshop on Bangla Language Processing (BLP-2025)",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.banglalp-1.15/",
    pages = "180--195",
    ISBN = "979-8-89176-314-2",
    abstract = "Detecting hateful memes is a complex task due to the interplay of text and visuals, with subtle cultural cues often determining whether content is harmful. This challenge is amplified in Bangla, a low-resource language where existing resources provide only binary labels or single dimensions of hate. To bridge this gap, we introduce BanHateME, a comprehensive Bangla hateful meme dataset with hierarchical annotations across three levels: binary hate, hate categories, and targeted groups. The dataset comprises 3,819 culturally grounded memes, annotated with substantial inter-annotator agreement. We further propose a hierarchical loss function that balances predictions across levels, preventing bias toward binary detection at the expense of fine-grained classification. To assess performance, we pair pretrained language and vision models and systematically evaluate three multimodal fusion strategies: summation, concatenation, and co-attention, demonstrating the effectiveness of hierarchical learning and cross-modal alignment. Our work establishes BanHateME as a foundational resource for fine-grained multimodal hate detection in Bangla and contributes key insights for content moderation in low-resource settings."
}