# NewsClaims: A New Benchmark for Claim Detection from News with Attribute Knowledge

arXiv: https://arxiv.org/abs/2112.08544 · HTML: https://mlsanity.com/paper/2112.08544/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 53 references that are in the map.
- First posted on arXiv 16 Dec 2021.

## Builds on (most cited first, 30 of 35)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [Making Pre-trained Language Models Better Few-shot Learners](https://mlsanity.com/paper/2012.15723/) (2020)
- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners](https://mlsanity.com/paper/2009.07118/) (2020)
- [AllenNLP: A Deep Semantic Natural Language Processing Platform](https://mlsanity.com/paper/1803.07640/) (2018)
- [Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach](https://mlsanity.com/paper/1909.00161/) (2019)
- [Explainable Automated Fact-Checking for Public Health Claims](https://mlsanity.com/paper/2010.09926/) (2020)
- [MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims](https://mlsanity.com/paper/1909.03242/) (2019)
- [FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information](https://mlsanity.com/paper/2106.05707/) (2021)
- [Automated Fact-Checking for Assisting Human Fact-Checkers](https://mlsanity.com/paper/2103.07769/) (2021)
- [COVID-Fact: Fact Extraction and Verification of Real-World Claims on COVID-19 Pandemic](https://mlsanity.com/paper/2106.03794/) (2021)
- [Zero-Shot Stance Detection: A Dataset and Model using Generalized Topic Representations](https://mlsanity.com/paper/2010.03640/) (2020)
- [That is a Known Lie: Detecting Previously Fact-Checked Claims](https://mlsanity.com/paper/2005.06058/) (2020)
- [A Survey on Stance Detection for Mis- and Disinformation Identification](https://mlsanity.com/paper/2103.00242/) (2021)
- [A Richly Annotated Corpus for Different Tasks in Automated Fact-Checking](https://mlsanity.com/paper/1911.01214/) (2019)
- [Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society](https://mlsanity.com/paper/2005.00033/) (2020)
- [From Arguments to Key Points: Towards Automatic Argument Summarization](https://mlsanity.com/paper/2005.01619/) (2020)
- [Cross-topic Argument Mining from Heterogeneous Sources Using Attention-based Neural Networks](https://mlsanity.com/paper/1802.05758/) (2018)
- [Corpus Wide Argument Mining -- a Working Solution](https://mlsanity.com/paper/1911.10763/) (2019)
- [Cross-Domain Label-Adaptive Stance Detection](https://mlsanity.com/paper/2104.07467/) (2021)
- [Fully Automated Fact Checking Using External Sources](https://mlsanity.com/paper/1710.00341/) (2017)
- [ClaimRank: Detecting Check-Worthy Claims in Arabic and English](https://mlsanity.com/paper/1804.07587/) (2018)
- [A Benchmark Dataset of Check-worthy Factual Claims](https://mlsanity.com/paper/2004.14425/) (2020)

## Built on by (most cited first, 20 of 20)

- [Factuality Challenges in the Era of Large Language Models](https://mlsanity.com/paper/2310.05189/) (2023)
- [AFaCTA: Assisting the Annotation of Factual Claim Detection with Reliable LLM Annotators](https://mlsanity.com/paper/2402.11073/) (2024)
- [EDIS: Entity-Driven Image Search over Multimodal Web Content](https://mlsanity.com/paper/2305.13631/) (2023)
- [Generative Large Language Models in Automated Fact-Checking: A Survey](https://mlsanity.com/paper/2407.02351/) (2024)
- [From Chaos to Clarity: Claim Normalization to Empower Fact-Checking](https://mlsanity.com/paper/2310.14338/) (2023)
- [LAraBench: Benchmarking Arabic AI with Large Language Models](https://mlsanity.com/paper/2305.14982/) (2023)
- [Decoding the Silent Majority: Inducing Belief Augmented Social Graph with Large Language Model for Response Forecasting](https://mlsanity.com/paper/2310.13297/) (2023)
- [KALM: Knowledge-Aware Integration of Local, Document, and Global Contexts for Long Document Understanding](https://mlsanity.com/paper/2210.04105/) (2022)
- [SmartBook: AI-Assisted Situation Report Generation for Intelligence Analysts](https://mlsanity.com/paper/2303.14337/) (2023)
- [Claim Check-Worthiness Detection: How Well do LLMs Grasp Annotation Guidelines?](https://mlsanity.com/paper/2404.12174/) (2024)
- [Lost in Translation, Found in Spans: Identifying Claims in Multilingual Social Media](https://mlsanity.com/paper/2310.18205/) (2023)
- [Veri-R1: Toward Precise and Faithful Claim Verification via Online Reinforcement Learning](https://mlsanity.com/paper/2510.01932/) (2025)
- [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)
- [Multilingual Models for Check-Worthy Social Media Posts Detection](https://mlsanity.com/paper/2408.06737/) (2024)
- [VERITAS-NLI : Validation and Extraction of Reliable Information Through Automated Scraping and Natural Language Inference](https://mlsanity.com/paper/2410.09455/) (2024)
- [Self-Adaptive Paraphrasing and Preference Learning for Improved Claim Verifiability](https://mlsanity.com/paper/2412.11653/) (2024)
- [When Misinformation Speaks and Converses: Rethinking Fact-Checking in Audio Platforms](https://mlsanity.com/paper/2604.16767/) (2026)
- [Ontology Enhanced Claim Detection](https://mlsanity.com/paper/2402.12282/) (2024)
- [ClaimPT: A Portuguese Dataset of Annotated Claims in News Articles](https://mlsanity.com/paper/2601.19490/) (2026)
- [Multilingual and Cross-Lingual Citation Needed Detection on Wikipedia for Lower-Resource Languages](https://mlsanity.com/paper/2605.31136/) (2026)

## Abstract (arXiv)

Claim detection and verification are crucial for news understanding and have emerged as promising technologies for mitigating misinformation and disinformation in the news. However, most existing work has focused on claim sentence analysis while overlooking additional crucial attributes (e.g., the claimer and the main object associated with the claim). In this work, we present NewsClaims, a new benchmark for attribute-aware claim detection in the news domain. We extend the claim detection problem to include extraction of additional attributes related to each claim and release 889 claims annotated over 143 news articles. NewsClaims aims to benchmark claim detection systems in emerging scenarios, comprising unseen topics with little or no training data. To this end, we see that zero-shot and prompt-based baselines show promising performance on this benchmark, while still considerably behind human performance.