# A Benchmark Dataset of Check-worthy Factual Claims

arXiv: https://arxiv.org/abs/2004.14425 · HTML: https://mlsanity.com/paper/2004.14425/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2020.

## Built on by (most cited first, 27 of 27)

- [Automated Fact-Checking: A Survey](https://mlsanity.com/paper/2109.11427/) (2021)
- [AFaCTA: Assisting the Annotation of Factual Claim Detection with Reliable LLM Annotators](https://mlsanity.com/paper/2402.11073/) (2024)
- [Check-COVID: Fact-Checking COVID-19 News Claims with Scientific Evidence](https://mlsanity.com/paper/2305.18265/) (2023)
- [NewsClaims: A New Benchmark for Claim Detection from News with Attribute Knowledge](https://mlsanity.com/paper/2112.08544/) (2021)
- [Generative Large Language Models in Automated Fact-Checking: A Survey](https://mlsanity.com/paper/2407.02351/) (2024)
- [Towards Effective Extraction and Evaluation of Factual Claims](https://mlsanity.com/paper/2502.10855/) (2025)
- [VerifAI: Verified Generative AI](https://mlsanity.com/paper/2307.02796/) (2023)
- [Dataset of Fake News Detection and Fact Verification: A Survey](https://mlsanity.com/paper/2111.03299/) (2021)
- [Environmental Claim Detection](https://mlsanity.com/paper/2209.00507/) (2022)
- [SemEval-2025 Task 7: Multilingual and Crosslingual Fact-Checked Claim Retrieval](https://mlsanity.com/paper/2505.10740/) (2025)
- [Atomic Self-Consistency for Better Long Form Generations](https://mlsanity.com/paper/2405.13131/) (2024)
- [MetaSumPerceiver: Multimodal Multi-Document Evidence Summarization for Fact-Checking](https://mlsanity.com/paper/2407.13089/) (2024)
- [Document-level Claim Extraction and Decontextualisation for Fact-Checking](https://mlsanity.com/paper/2406.03239/) (2024)
- [ProCoT: Stimulating Critical Thinking and Writing of Students through Engagement with Large Language Models (LLMs)](https://mlsanity.com/paper/2312.09801/) (2023)
- [ViClaim: A Multilingual Multilabel Dataset for Automatic Claim Detection in Videos](https://mlsanity.com/paper/2504.12882/) (2025)
- [Cross-lingual Transfer Learning for Check-worthy Claim Identification over Twitter](https://mlsanity.com/paper/2211.05087/) (2022)
- [Multilingual and Multi-topical Benchmark of Fine-tuned Language models and Large Language Models for Check-Worthy Claim Detection](https://mlsanity.com/paper/2311.06121/) (2023)
- [Scope of Large Language Models for Mining Emerging Opinions in Online Health Discourse](https://mlsanity.com/paper/2403.03336/) (2024)
- [Tailoring Vaccine Messaging with Common-Ground Opinions](https://mlsanity.com/paper/2405.10861/) (2024)
- [Atomic Consistency Preference Optimization for Long-Form Question Answering](https://mlsanity.com/paper/2505.09039/) (2025)
- [Benchmarking Large Language Models for Knowledge Graph Validation](https://mlsanity.com/paper/2602.10748/) (2026)
- [Leveraging Commonsense Knowledge on Classifying False News and Determining Checkworthiness of Claims](https://mlsanity.com/paper/2108.03731/) (2021)
- [MythQA: Query-Based Large-Scale Check-Worthy Claim Detection through Multi-Answer Open-Domain Question Answering](https://mlsanity.com/paper/2307.11848/) (2023)
- [Ontology Enhanced Claim Detection](https://mlsanity.com/paper/2402.12282/) (2024)
- [HYBRINFOX at CheckThat! 2024 -- Task 1: Enhancing Language Models with Structured Information for Check-Worthiness Estimation](https://mlsanity.com/paper/2407.03850/) (2024)
- [IAI Group at CheckThat! 2024: Transformer Models and Data Augmentation for Checkworthy Claim Detection](https://mlsanity.com/paper/2408.01118/) (2024)
- [Examining the Metrics for Document-Level Claim Extraction in Czech and Slovak](https://mlsanity.com/paper/2511.14566/) (2025)

## Abstract (arXiv)

In this paper we present the ClaimBuster dataset of 23,533 statements extracted from all U.S. general election presidential debates and annotated by human coders. The ClaimBuster dataset can be leveraged in building computational methods to identify claims that are worth fact-checking from the myriad of sources of digital or traditional media. The ClaimBuster dataset is publicly available to the research community, and it can be found at http://doi.org/10.5281/zenodo.3609356.