@inproceedings{zhang-etal-2026-cover,
title = "{C}o{V}er: Conflict-aware Claim Verification",
author = "Zhang, Shuning and
Shi, Dai and
Chu, Bohao and
Wang, Hui and
Chuai, Yuwei and
Wang, Yifan and
Chen, Jingruo and
Li, Simin and
Yi, Xin and
Li, Hewu",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.829/",
pages = "19273--19289",
ISBN = "979-8-89176-459-0",
abstract = "Social media fact-checking is increasingly challenged by evidence-level and aggregation-level conflicts, where erroneous evidence often mimics authoritative news sources. To support conflict verification task, we construct ContraNote, a real-world dataset derived from X Community Notes. ContraNote is curated to mirror the two aforementioned challenges, providing 33,686 posts for evaluating evidence-level conflict resolution, and 54,474 instances for evaluating aggregation-level evidence prioritization. This paper further proposes CoVer, a framework designed to resolve these contradictions through factual adjudication. Cover uses a three-stage pipeline consisting of evidence schema normalization, factual consensus and support verification, prioritizing definitive evidence over noise to prevent it from compromising the final verdict. Technical evaluations across multiple benchmarks show that CoVer outperforms state-of-the-art baselines, achieving an accuracy of 88.4{\%} on CONFACT-HumC and 89.4{\%} on CONFACT-ModC."
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<abstract>Social media fact-checking is increasingly challenged by evidence-level and aggregation-level conflicts, where erroneous evidence often mimics authoritative news sources. To support conflict verification task, we construct ContraNote, a real-world dataset derived from X Community Notes. ContraNote is curated to mirror the two aforementioned challenges, providing 33,686 posts for evaluating evidence-level conflict resolution, and 54,474 instances for evaluating aggregation-level evidence prioritization. This paper further proposes CoVer, a framework designed to resolve these contradictions through factual adjudication. Cover uses a three-stage pipeline consisting of evidence schema normalization, factual consensus and support verification, prioritizing definitive evidence over noise to prevent it from compromising the final verdict. Technical evaluations across multiple benchmarks show that CoVer outperforms state-of-the-art baselines, achieving an accuracy of 88.4% on CONFACT-HumC and 89.4% on CONFACT-ModC.</abstract>
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%0 Conference Proceedings
%T CoVer: Conflict-aware Claim Verification
%A Zhang, Shuning
%A Shi, Dai
%A Chu, Bohao
%A Wang, Hui
%A Chuai, Yuwei
%A Wang, Yifan
%A Chen, Jingruo
%A Li, Simin
%A Yi, Xin
%A Li, Hewu
%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 zhang-etal-2026-cover
%X Social media fact-checking is increasingly challenged by evidence-level and aggregation-level conflicts, where erroneous evidence often mimics authoritative news sources. To support conflict verification task, we construct ContraNote, a real-world dataset derived from X Community Notes. ContraNote is curated to mirror the two aforementioned challenges, providing 33,686 posts for evaluating evidence-level conflict resolution, and 54,474 instances for evaluating aggregation-level evidence prioritization. This paper further proposes CoVer, a framework designed to resolve these contradictions through factual adjudication. Cover uses a three-stage pipeline consisting of evidence schema normalization, factual consensus and support verification, prioritizing definitive evidence over noise to prevent it from compromising the final verdict. Technical evaluations across multiple benchmarks show that CoVer outperforms state-of-the-art baselines, achieving an accuracy of 88.4% on CONFACT-HumC and 89.4% on CONFACT-ModC.
%U https://aclanthology.org/2026.emnlp-main.829/
%P 19273-19289
Markdown (Informal)
[CoVer: Conflict-aware Claim Verification](https://aclanthology.org/2026.emnlp-main.829/) (Zhang et al., EMNLP 2026)
ACL
- Shuning Zhang, Dai Shi, Bohao Chu, Hui Wang, Yuwei Chuai, Yifan Wang, Jingruo Chen, Simin Li, Xin Yi, and Hewu Li. 2026. CoVer: Conflict-aware Claim Verification. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 19273–19289, Budapest, Hungary. Association for Computational Linguistics.