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CoVer: Conflict-Aware Claim Verification

arXiv 2609.00508 ↗First posted 1 Sep 2026cs.AIcited by 0

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Abstract (arXiv)

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.).

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 31 of its 49 references that are in the map. First posted on arXiv 1 Sep 2026.

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31 of 49 references.

References · 31

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2018FEVER: a large-scale dataset for Fact Extraction and VERificationcited by 1,362
  3. 2023Adaptive Chameleon or Stubborn Sloth: Revealing the Behavior of Large Language Models in Knowledge Conflictscited by 333
  4. 2023FacTool: Factuality Detection in Generative AI -- A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarioscited by 247
  5. 2022Fine-tuning language models to find agreement among humans with diverse preferencescited by 189
  6. 2019MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claimscited by 169
  7. 2023AVeriTeC: A Dataset for Real-world Claim Verification with Evidence from the Webcited by 144
  8. 2022Rich Knowledge Sources Bring Complex Knowledge Conflicts: Recalibrating Models to Reflect Conflicting Evidencecited by 142
  9. 2023Factuality Challenges in the Era of Large Language Modelscited by 125
  10. 2018DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learningcited by 100
  11. 2023Factcheck-Bench: Fine-Grained Evaluation Benchmark for Automatic Fact-checkerscited by 86
  12. 2024FaithEval: Can Your Language Model Stay Faithful to Context, Even If "The Moon is Made of Marshmallows"cited by 85
19 more
  1. 2024What Evidence Do Language Models Find Convincing?cited by 74
  2. 2024SNIFFER: Multimodal Large Language Model for Explainable Out-of-Context Misinformation Detectioncited by 73
  3. 2025Retrieval-Augmented Generation with Conflicting Evidencecited by 60
  4. 2024Blinded by Generated Contexts: How Language Models Merge Generated and Retrieved Contexts When Knowledge Conflicts?cited by 47
  5. 2023Reinforcement Learning-based Counter-Misinformation Response Generation: A Case Study of COVID-19 Vaccine Misinformationcited by 39
  6. 2024FIRE: Fact-checking with Iterative Retrieval and Verificationcited by 39
  7. 2015A Survey on Truth Discoverycited by 36
  8. 2024Evidence-Driven Retrieval Augmented Response Generation for Online Misinformationcited by 32
  9. 2024JustiLM: Few-shot Justification Generation for Explainable Fact-Checking of Real-world Claimscited by 29
  10. 2020The Role of the Crowd in Countering Misinformation: A Case Study of the COVID-19 Infodemiccited by 24
  11. 2024Supernotes: Driving Consensus in Crowd-Sourced Fact-Checkingcited by 22
  12. 2023Automated Claim Matching with Large Language Models: Empowering Fact-Checkers in the Fight Against Misinformationcited by 18
  13. 2024ECon: On the Detection and Resolution of Evidence Conflictscited by 18
  14. 2025Resolving Conflicting Evidence in Automated Fact-Checking: A Study on Retrieval-Augmented LLMscited by 16
  15. 2023Did the Roll-Out of Community Notes Reduce Engagement With Misinformation on X/Twitter?cited by 11
  16. 2025Question Answering under Temporal Conflict: Evaluating and Organizing Evolving Knowledge with LLMscited by 4
  17. 2026Consensus Stability of Community Notes on Xcited by 3
  18. 2026Collab: Fostering Critical Identification of Deepfake Videos on Social Media via Synergistic Annotationcited by 3
  19. 2023Is Fact-Checking Politically Neutral? Asymmetries in How U.S. Fact-Checking Organizations Pick Up False Statements Mentioning Political Elitescited by 1

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