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Explainable Automated Fact-Checking: A Survey

arXiv 2011.03870 ↗First posted 7 Nov 2020cs.CL cs.AIcited by 83

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Abstract (arXiv)

A number of exciting advances have been made in automated fact-checking thanks to increasingly larger datasets and more powerful systems, leading to improvements in the complexity of claims which can be accurately fact-checked. However, despite these advances, there are still desirable functionalities missing from the fact-checking pipeline. In this survey, we focus on the explanation functionality -- that is fact-checking systems providing reasons for their predictions. We summarize existing methods for explaining the predictions of fact-checking systems and we explore trends in this topic. Further, we consider what makes for good explanations in this specific domain through a comparative analysis of existing fact-checking explanations against some desirable properties. Finally, we propose further research directions for generating fact-checking explanations, and describe how these may lead to improvements in the research area.

Key facts. Cited by 83 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Nov 2020.

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Citing papers per year: 2021: 14; 2022: 13; 2023: 15; 2024: 18; 2025: 18; 2026: 5.

83 papers.

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  1. 2021A Survey on Automated Fact-Checkingcited by 376
  2. 2021FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured informationcited by 166
  3. 2021Automated Fact-Checking for Assisting Human Fact-Checkerscited by 144
  4. 2023AVeriTeC: A Dataset for Real-world Claim Verification with Evidence from the Webcited by 144
  5. 2021Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processingcited by 126
  6. 2022Detecting and Understanding Harmful Memes: A Surveycited by 58
  7. 2023FactKG: Fact Verification via Reasoning on Knowledge Graphscited by 57
  8. 2023Scientific Fact-Checking: A Survey of Resources and Approachescited by 53
  9. 2021ProoFVer: Natural Logic Theorem Proving for Fact Verificationcited by 52
  10. 2023Multimodal Automated Fact-Checking: A Surveycited by 51
  11. 2024Explainable Fake News Detection With Large Language Model via Defense Among Competing Wisdomcited by 49
  12. 2021A Survey on Multimodal Disinformation Detectioncited by 47
38 more
  1. 2022Missing Counter-Evidence Renders NLP Fact-Checking Unrealistic for Misinformationcited by 45
  2. 2022Stretching Sentence-pair NLI Models to Reason over Long Documents and Clusterscited by 44
  3. 2022CHEF: A Pilot Chinese Dataset for Evidence-Based Fact-Checkingcited by 37
  4. 2023Explainable Claim Verification via Knowledge-Grounded Reasoning with Large Language Modelscited by 37
  5. 2023HealthFC: Verifying Health Claims with Evidence-Based Medical Fact-Checkingcited by 35
  6. 2024Claim Detection for Automated Fact-checking: A Survey on Monolingual, Multilingual and Cross-Lingual Researchcited by 35
  7. 2021Is Sparse Attention more Interpretable?cited by 31
  8. 2023The Perils & Promises of Fact-checking with Large Language Modelscited by 31
  9. 2022CoVERT: A Corpus of Fact-checked Biomedical COVID-19 Tweetscited by 29
  10. 2024JustiLM: Few-shot Justification Generation for Explainable Fact-Checking of Real-world Claimscited by 29
  11. 2025Show Me the Work: Fact-Checkers' Requirements for Explainable Automated Fact-Checkingcited by 29
  12. 2023Benchmarking the Generation of Fact Checking Explanationscited by 25
  13. 2024Evidence-backed Fact Checking using RAG and Few-Shot In-Context Learning with LLMscited by 23
  14. 2023Fact-checking information from large language models can decrease headline discernmentcited by 22
  15. 2022ProtoTEx: Explaining Model Decisions with Prototype Tensorscited by 21
  16. 2021A Survey on Predicting the Factuality and the Bias of News Mediacited by 20
  17. 2023The Intended Uses of Automated Fact-Checking Artefacts: Why, How and Whocited by 19
  18. 2024Stress-Testing Long-Context Language Models with Lifelong ICL and Task Haystackcited by 19
  19. 2024Pelican: Correcting Hallucination in Vision-LLMs via Claim Decomposition and Program of Thought Verificationcited by 17
  20. 2021Explainable Fact-checking through Question Answeringcited by 16
  21. 2024Re-Search for The Truth: Multi-round Retrieval-augmented Large Language Models are Strong Fake News Detectorscited by 16
  22. 2021Generating Fluent Fact Checking Explanations with Unsupervised Post-Editingcited by 15
  23. 2022Matching Tweets With Applicable Fact-Checks Across Languagescited by 15
  24. 2022Exploring Faithful Rationale for Multi-hop Fact Verification via Salience-Aware Graph Learningcited by 15
  25. 2024Automated Justification Production for Claim Veracity in Fact Checking: A Survey on Architectures and Approachescited by 15
  26. 2022A Coarse-to-fine Cascaded Evidence-Distillation Neural Network for Explainable Fake News Detectioncited by 12
  27. 2021Dataset of Fake News Detection and Fact Verification: A Surveycited by 11
  28. 2025SemEval-2025 Task 7: Multilingual and Crosslingual Fact-Checked Claim Retrievalcited by 11
  29. 2021FacTeR-Check: Semi-automated fact-checking through Semantic Similarity and Natural Language Inferencecited by 10
  30. 2022Converge to the Truth: Factual Error Correction via Iterative Constrained Editingcited by 9
  31. 2023Countering Misinformation via Emotional Response Generationcited by 9
  32. 2023Little Giants: Exploring the Potential of Small LLMs as Evaluation Metrics in Summarization in the Eval4NLP 2023 Shared Taskcited by 8
  33. 2024Towards a Framework for Evaluating Explanations in Automated Fact Verificationcited by 7
  34. 2025Can LLMs Automate Fact-Checking Article Writing?cited by 7
  35. 2021Graph Reasoning with Context-Aware Linearization for Interpretable Fact Extraction and Verificationcited by 6
  36. 2025Mechanistic Interpretability Needs Philosophycited by 6
  37. 2022Active PETs: Active Data Annotation Prioritisation for Few-Shot Claim Verification with Pattern Exploiting Trainingcited by 5
  38. 2023Do not Mask Randomly: Effective Domain-adaptive Pre-training by Masking In-domain Keywordscited by 5

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