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        <title>OVLAFND: Open-Vocabulary Logic-Aware Fake News Detection</title>
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        <namePart type="given">Xiaoya</namePart>
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            <namePart type="given">Hung-Yi</namePart>
            <namePart type="family">Lee</namePart>
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    <abstract>Multimodal fake news detection is critical for maintaining the integrity of social media. Current models often rely on shallow cross-modal correlations and rigid domain labels, which limits robustness to logical inconsistency and domain shift. We propose the Open-Vocabulary and Logic-Aware Fake News Detection (OVLAFND) framework to address these issues and relax traditional categorization constraints. To model fine-grained logical conflicts rather than surface relevance, our framework introduces Logical Adversarial Data Augmentation (LADA), a multimodal LLM-driven pipeline that constructs counterfactual text-image pairs as hard negatives. Then, we propose a Logic Consistency Module (LCM) that uses these adversarial signals to learn a dedicated logic-consistency subspace for assessing whether textual claims are supported by visual evidence. To improve topic flexibility, we transform the rigid domain labels into an Open-Vocabulary Topic Manifold (OVTM) to achieve highly interpretable and label-free topic reasoning. Across Weibo and Weibo-21, OVLAFND achieves competitive fixed-domain performance and better leave-one-domain-out generalization. Our code is available at https://github.com/lllfxy/OVLAFND.</abstract>
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%0 Conference Proceedings
%T OVLAFND: Open-Vocabulary Logic-Aware Fake News Detection
%A Fang, Xiaoya
%A Chen, Lin
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F fang-chen-2026-ovlafnd
%X Multimodal fake news detection is critical for maintaining the integrity of social media. Current models often rely on shallow cross-modal correlations and rigid domain labels, which limits robustness to logical inconsistency and domain shift. We propose the Open-Vocabulary and Logic-Aware Fake News Detection (OVLAFND) framework to address these issues and relax traditional categorization constraints. To model fine-grained logical conflicts rather than surface relevance, our framework introduces Logical Adversarial Data Augmentation (LADA), a multimodal LLM-driven pipeline that constructs counterfactual text-image pairs as hard negatives. Then, we propose a Logic Consistency Module (LCM) that uses these adversarial signals to learn a dedicated logic-consistency subspace for assessing whether textual claims are supported by visual evidence. To improve topic flexibility, we transform the rigid domain labels into an Open-Vocabulary Topic Manifold (OVTM) to achieve highly interpretable and label-free topic reasoning. Across Weibo and Weibo-21, OVLAFND achieves competitive fixed-domain performance and better leave-one-domain-out generalization. Our code is available at https://github.com/lllfxy/OVLAFND.
%U https://aclanthology.org/2026.emnlp-main.361/
%P 8267-8280

Markdown (Informal)

[OVLAFND: Open-Vocabulary Logic-Aware Fake News Detection](https://aclanthology.org/2026.emnlp-main.361/) (Fang & Chen, EMNLP 2026)

• OVLAFND: Open-Vocabulary Logic-Aware Fake News Detection (Fang & Chen, EMNLP 2026)

ACL

• Xiaoya Fang and Lin Chen. 2026. OVLAFND: Open-Vocabulary Logic-Aware Fake News Detection. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 8267–8280, Budapest, Hungary. Association for Computational Linguistics.