@inproceedings{yang-etal-2026-dialectical,
    title = "Dialectical Structured Reasoning for Explainable Multimodal Fake News Detection",
    author = "Yang, Ruichao  and
      Bian, Yufan  and
      Gao, Wei  and
      Zhang, Bo-Wen  and
      Ma, Jing  and
      Lin, Hongzhan  and
      Luo, Ziyang  and
      Zhu, Xiaobin  and
      Yin, Xu-Cheng",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1601/",
    pages = "31998--32013",
    ISBN = "979-8-89176-395-1",
    abstract = "Current multimodal fake news detectors predominantly function as opaque classifiers, offering limited deductive transparency and little insight into how conflicting evidence is reconciled. To address this limitation, we propose Dialectical Structured Reasoning (DSR), a framework modeling fake news detection as an explicit dialectical process over multimodal social context. DSR instantiates two opposing agents: a Verifier, which constructs evidence paths supporting semantic consistency, and a Debunker, which actively explores exposing logical or factual contradictions. Then a differentiable Judge agent adjudicates between these competing perspectives by integrating local evidence with global parametric knowledge. Experiments on three benchmarks demonstrate that DSR achieves state-of-the-art performance while producing transparent, dialectically grounded explanations that closely mirror human reasoning process."
}