@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Dialectical Structured Reasoning for Explainable Multimodal Fake News Detection
%A Yang, Ruichao
%A Bian, Yufan
%A Gao, Wei
%A Zhang, Bo-Wen
%A Ma, Jing
%A Lin, Hongzhan
%A Luo, Ziyang
%A Zhu, Xiaobin
%A Yin, Xu-Cheng
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F yang-etal-2026-dialectical
%X 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.
%U https://aclanthology.org/2026.findings-acl.1601/
%P 31998-32013
Markdown (Informal)
[Dialectical Structured Reasoning for Explainable Multimodal Fake News Detection](https://aclanthology.org/2026.findings-acl.1601/) (Yang et al., Findings 2026)
ACL
- Ruichao Yang, Yufan Bian, Wei Gao, Bo-Wen Zhang, Jing Ma, Hongzhan Lin, Ziyang Luo, Xiaobin Zhu, and Xu-Cheng Yin. 2026. Dialectical Structured Reasoning for Explainable Multimodal Fake News Detection. In Findings of the Association for Computational Linguistics: ACL 2026, pages 31998–32013, San Diego, California, United States. Association for Computational Linguistics.