Problem
Research questions and friction points this paper is trying to address.
evidence-level conflict
aggregation-level conflict
social media fact-checking
CoVer: Conflict-Aware Claim Verification
🤖 AI Summary
本文提出CoVer框架，通过证据规范化、事实共识和支持验证三阶段解决社交媒体事实核查中的证据级和聚合级冲突问题。
📝 Abstract
Social media fact-checking has long been challenged by evidence-level and aggregation-level conflicts, where erroneous evidence mimics authoritative news sources. To capture this challenge and support conflict verification tasks, we present ContraNote, a large-scale real-world dataset curated from X's Community Notes system. It includes 33,686 posts for evaluating evidence-level conflict resolution, and 54,474 instances for evaluating aggregation-level prioritization. Additionally, we propose CoVer, a factual adjudication framework with three-stage pipelines: evidence schema normalization, factual consensus and support verification. This prioritizes evidence over noise to prevent it from compromising the final verdict. Technical evaluations show that CoVer achieves strong performance compared with state-of-the-art baselines across ContraNote (86.0% Acc., 68.0% mac. F1, 64.5 bal. Acc. on Conflict; and 88.5% Acc., 88.5 mac. F1 and 89.2 bal. Acc. on Prioritization), CONFACT-HumC (88.4% Acc.) and CONFACT-ModC (89.4% Acc.).
Innovation
Methods, ideas, or system contributions that make the work stand out.
Conflict-Aware
Claim Verification
Evidence Schema Normalization
Factual Consensus
Support Verification
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