mlsanityData to 7 Oct 2026

Assisting the Human Fact-Checkers: Detecting All Previously Fact-Checked Claims in a Document

arXiv 2109.07410 ↗First posted 14 Sep 2021cs.CL cs.AI cs.CYcited by 19

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Abstract (arXiv)

Given the recent proliferation of false claims online, there has been a lot of manual fact-checking effort. As this is very time-consuming, human fact-checkers can benefit from tools that can support them and make them more efficient. Here, we focus on building a system that could provide such support. Given an input document, it aims to detect all sentences that contain a claim that can be verified by some previously fact-checked claims (from a given database). The output is a re-ranked list of the document sentences, so that those that can be verified are ranked as high as possible, together with corresponding evidence. Unlike previous work, which has looked into claim retrieval, here we take a document-level perspective. We create a new manually annotated dataset for this task, and we propose suitable evaluation measures. We further experiment with a learning-to-rank approach, achieving sizable performance gains over several strong baselines. Our analysis demonstrates the importance of modeling text similarity and stance, while also taking into account the veracity of the retrieved previously fact-checked claims. We believe that this research would be of interest to fact-checkers, journalists, media, and regulatory authorities.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 24 of its 52 references that are in the map. First posted on arXiv 14 Sep 2021.

Builds on

24 of 52 references.

References · 24

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  2. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
  3. 2021SimCSE: Simple Contrastive Learning of Sentence Embeddingscited by 2,168
  4. 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,242
  5. 2019Adversarial NLI: A New Benchmark for Natural Language Understandingcited by 798
  6. 2017Fake News Detection on Social Media: A Data Mining Perspectivecited by 334
  7. 2020Pretrained Transformers for Text Ranking: BERT and Beyondcited by 266
  8. 2021Automated Fact-Checking for Assisting Human Fact-Checkerscited by 144
  9. 2020FANG: Leveraging Social Context for Fake News Detection Using Graph Representationcited by 98
  10. 2018Automated Fact Checking: Task formulations, methods and future directionscited by 96
  11. 2020That is a Known Lie: Detecting Previously Fact-Checked Claimscited by 82
  12. 2018Towards Automated Factchecking: Developing an Annotation Schema and Benchmark for Consistent Automated Claim Detectioncited by 55
12 more
  1. 2020Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake Newscited by 48
  2. 2021A Survey on Multimodal Disinformation Detectioncited by 47
  3. 2020Overview of CheckThat! 2020: Automatic Identification and Verification of Claims in Social Mediacited by 46
  4. 2021Claim Matching Beyond English to Scale Global Fact-Checkingcited by 42
  5. 2018Overview of the CLEF-2018 CheckThat! Lab on Automatic Identification and Verification of Political Claims. Task 1: Check-Worthinesscited by 41
  6. 2015A Survey on Truth Discoverycited by 36
  7. 2019Simple Applications of BERT for Ad Hoc Document Retrievalcited by 34
  8. 2020CheckThat! at CLEF 2020: Enabling the Automatic Identification and Verification of Claims in Social Mediacited by 25
  9. 2018The Rise of Guardians: Fact-checking URL Recommendation to Combat Fake Newscited by 24
  10. 2021The Role of Context in Detecting Previously Fact-Checked Claimscited by 17
  11. 2021Topic-Aware Evidence Reasoning and Stance-Aware Aggregation for Fact Verificationcited by 16
  12. 2019Automated Fact Checking in the News Roomcited by 13

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 5; 2023: 4; 2024: 2; 2025: 5; 2026: 3.

19 papers.

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  1. 2022Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Surveycited by 80
  2. 2024Claim Detection for Automated Fact-checking: A Survey on Monolingual, Multilingual and Cross-Lingual Researchcited by 35
  3. 2023Multilingual Previously Fact-Checked Claim Retrievalcited by 32
  4. 2024FACT-GPT: Fact-Checking Augmentation via Claim Matching with LLMscited by 27
  5. 2023From Chaos to Clarity: Claim Normalization to Empower Fact-Checkingcited by 17
  6. 2025Zero-shot and Few-shot Learning with Instruction-following LLMs for Claim Matching in Automated Fact-checkingcited by 12
  7. 2022Monant Medical Misinformation Dataset: Mapping Articles to Fact-Checked Claimscited by 10
  8. 2022CrowdChecked: Detecting Previously Fact-Checked Claims in Social Mediacited by 9
  9. 2023Lost in Translation, Found in Spans: Identifying Claims in Multilingual Social Mediacited by 8
  10. 2025Large Language Models for Multilingual Previously Fact-Checked Claim Detectioncited by 6
  11. 2022Harnessing Abstractive Summarization for Fact-Checked Claim Detectioncited by 5
  12. 2023Breaking Language Barriers with MMTweets: Advancing Cross-Lingual Debunked Narrative Retrieval for Fact-Checkingcited by 5
7 more
  1. 2025When Claims Evolve: Evaluating and Enhancing the Robustness of Embedding Models Against Misinformation Editscited by 3
  2. 2022Z-Index at CheckThat! Lab 2022: Check-Worthiness Identification on Tweet Textcited by 1
  3. 2025A Generative-AI-Driven Claim Retrieval System Capable of Detecting and Retrieving Claims from Social Media Platforms in Multiple Languagescited by 1
  4. 2025Investigating Language and Retrieval Bias in Multilingual Previously Fact-Checked Claim Detectioncited by 1
  5. 2026MERMAID: Memory-Enhanced Retrieval and Reasoning with Multi-Agent Iterative Knowledge Grounding for Veracity Assessmentcited by 0
  6. 2026CritiSense: Critical Digital Literacy and Resilience Against Misinformationcited by 0
  7. 2026DS@GT ARC at CheckThat! 2026: LLM-Based Trace Ranking and Grouped Reward Modeling for Multilingual Numerical Claim Verificationcited by 0