@inproceedings{thapa-etal-2026-multimodal,
    title = "Multimodal Identification of Vaccine Content Stance on Social Media",
    author = {Thapa, Surendrabikram  and
      Shiwakoti, Shuvam  and
      Shah, Siddhant Bikram  and
      Rauniyar, Kritesh  and
      Thapa, Laxmi  and
      Adhikari, Surabhi  and
      Johnson, Kristina T.  and
      H{\"u}rriyeto{\u{g}}lu, Ali  and
      Tanev, Hristo  and
      Naseem, Usman},
    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-1.3/",
    pages = "17--25",
    ISBN = "979-8-89176-402-6",
    abstract = "Vaccination-related memes on social media play an increasingly influential role in shaping public perception of immunization, often spreading both supportive messaging and vaccine-critical narratives through multimodal communication. Detecting such content is challenging due to the combined use of images, embedded text, sarcasm, humor, and cultural references. This paper presents an overview of the Shared Task on Multimodal Identification of Vaccine Critical Content on Social Media, organized as part of the 9th Workshop on Event Extraction and Understanding: Challenges and Applications (EEUCA 2026) at ACL 2026. The task is based on the VaxMeme dataset, a large-scale collection of vaccination-related memes annotated into three classes: Vaccine-critical, Neutral, and Pro-vaccine. A total of 77 participants registered for the competition, with 25 teams submitting systems for evaluation. Participating approaches included transformer-based multimodal architectures, vision-language models, ensemble methods, and instruction-tuned large language models. The best-performing system achieved a macro F1-score of 0.8494. This shared task provides insights into the strengths and limitations of current multimodal approaches for vaccine stance detection and highlights future directions for robust public health misinformation analysis."
}