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    <abstract>Social media platforms have become critical arenas for public discourse, yet existing stance detection methods often reduce opinions to surface-level labels, overlooking the conversational evidence behind stance expressions. We introduce Conversational Stance-Cause Pair Detection (CSCPD), a new task that jointly identifies both the stance polarity and its observable contextual evidence within multi-turn conversations. To advance research in this direction, we present Cause-CSD, the first large-scale dataset for CSCPD, spanning 21,048 annotated stance-cause pairs across diverse open-domain, textual, and multimodal discussions. We further propose Stance-Cause Detection Language Model (SCD-LM), a unified language model framework that leverages explicit context reasoning and joint decoding to predict stances and their supporting causes, along with human-readable rationales. Extensive experiments demonstrate that SCD-LM achieves state-of-the-art results on both text-only and multimodal subtasks, significantly outperforming strong baselines, especially for long-range and image-grounded cause detection. Our work advances explainable stance analysis and underpins understanding of public opinion drivers in impactful online settings.</abstract>
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%0 Conference Proceedings
%T Cause-CSD: A Challenge Multimodal Conversational Stance Cause Detection Dataset and Effective Method
%A Niu, Fuqiang
%A Zhang, Bowen
%A Zhu, Junting
%A Liao, Qing
%A Dai, Genan
%A Huang, Hu
%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 niu-etal-2026-cause
%X Social media platforms have become critical arenas for public discourse, yet existing stance detection methods often reduce opinions to surface-level labels, overlooking the conversational evidence behind stance expressions. We introduce Conversational Stance-Cause Pair Detection (CSCPD), a new task that jointly identifies both the stance polarity and its observable contextual evidence within multi-turn conversations. To advance research in this direction, we present Cause-CSD, the first large-scale dataset for CSCPD, spanning 21,048 annotated stance-cause pairs across diverse open-domain, textual, and multimodal discussions. We further propose Stance-Cause Detection Language Model (SCD-LM), a unified language model framework that leverages explicit context reasoning and joint decoding to predict stances and their supporting causes, along with human-readable rationales. Extensive experiments demonstrate that SCD-LM achieves state-of-the-art results on both text-only and multimodal subtasks, significantly outperforming strong baselines, especially for long-range and image-grounded cause detection. Our work advances explainable stance analysis and underpins understanding of public opinion drivers in impactful online settings.
%U https://aclanthology.org/2026.findings-acl.1925/
%P 38654-38666

Markdown (Informal)

[Cause-CSD: A Challenge Multimodal Conversational Stance Cause Detection Dataset and Effective Method](https://aclanthology.org/2026.findings-acl.1925/) (Niu et al., Findings 2026)

• Cause-CSD: A Challenge Multimodal Conversational Stance Cause Detection Dataset and Effective Method (Niu et al., Findings 2026)

ACL

• Fuqiang Niu, Bowen Zhang, Junting Zhu, Qing Liao, Genan Dai, and Hu Huang. 2026. Cause-CSD: A Challenge Multimodal Conversational Stance Cause Detection Dataset and Effective Method. In Findings of the Association for Computational Linguistics: ACL 2026, pages 38654–38666, San Diego, California, United States. Association for Computational Linguistics.