@inproceedings{acharya-etal-2026-linus,
    title = "Linus@{EEUCA} 2026: Multimodal and Text-Only Approaches to Vaccine-Critical Meme Detection.",
    author = "Acharya, Darwin  and
      Saud, Shiv Ram  and
      Regmi, Sunil",
    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-1.25/",
    pages = "223--232",
    ISBN = "979-8-89176-402-6",
    abstract = "In this paper, we describe our participation in the Shared Task on Multimodal Identification of Vaccine Critical Content on Social Media (VaxMeme) of EEUCA 2026, a satellite of ACL 2026. We tackle the classification of Twitter-based vaccine memes into anti-vaccine, neutral, and pro-vaccine categories using the VaxMeme dataset with 8,195 train, 1,024 val, and 1,025 test samples. We experiment with two different architecture families: (i) Multimodal hybrids: CLIP ViT-B/32 for images + BERT-based models for texts (BERT-base-uncased, ModernBERT) with late fusion strategy based on concatenation of L2-normalized feature vectors and (ii) Text-only: pre-trained models for texts (BERT-base-uncased, RoBERTa-base, ModernBERT-base, DistilBERT-base, Deberta-v3-base) for post{\_}text. In both cases, we use a three-layer feed-forward network with GELU activation for classification. We use class-weighted cross-entropy loss, differential learning rates, AdamW optimizer, gradient accumulation, OneCycleLR scheduler, and early stopping on the val set for optimization. Data augmentation is applied for the multimodal CLIP-based approach only. The winning approach among those tested is the text-only BERT-base-uncased with a macro-F1 of 0.8102 which is ahead of the performance of the CLIP + BERT-base hybrid model, which achieves a test macro-F1 of 0.7603."
}