@inproceedings{zaman-etal-2026-cuet-synthetica,
    title = "{CUET}{\_}{SYNTHETICA}@{EEUCA} 2026: Gated Cross-Modal Attention with Domain-Adapted Text Encoding for Vaccine-Critical Meme Detection",
    author = "Zaman, Sumaiya  and
      Rishta, Miftahul Jannat  and
      Chowdhury, Shiti",
    editor = {H{\"u}rriyeto{\u{g}}lu, Ali  and
      Thapa, Surendrabikram  and
      Tanev, Hristo},
    booktitle = "Proceedings of the 9th Workshop on Event Extraction and Understanding: Challenges and Applications ({EEUCA} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.eeuca-1.14/",
    pages = "133--140",
    ISBN = "979-8-89176-402-6",
    abstract = "Vaccine-critical memes have emerged as a growing challenge for public health communication, combining images and text to spread misinformation in ways that are difficult to detect automatically. In this paper, we have described our system for the EEUCA 2026 Shared Task on Multimodal Vaccine-Critical Meme Detection, classifying memes from the VaxMeme dataset into Vaccine-Critical, Neutral and Pro-Vaccine categories. We have experimented with multiple text encoders and visual backbones, finding that Twitter-RoBERTa fused with CLIP ViT-L/14 through gated cross-modal attention has achieved a test macro F1 of 0.8357. We have further shown that domain-specific pretraining has outperformed larger general-purpose models, highlighting the importance of domain adaptation over raw model scale. Finally, our system has secured the 3rd position on the shared task leaderboard."
}