# A Revisit of Fake News Dataset with Augmented Fact-checking by ChatGPT

arXiv: https://arxiv.org/abs/2312.11870 · HTML: https://mlsanity.com/paper/2312.11870/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 41 references that are in the map.
- First posted on arXiv 19 Dec 2023.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Pre-trained Models for Natural Language Processing: A Survey](https://mlsanity.com/paper/2003.08271/) (2020)
- [A Survey on Automated Fact-Checking](https://mlsanity.com/paper/2108.11896/) (2021)
- ["Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection](https://mlsanity.com/paper/1705.00648/) (2017)
- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [Towards Debiasing Fact Verification Models](https://mlsanity.com/paper/1908.05267/) (2019)
- [The science of fake news](https://mlsanity.com/paper/2307.07903/) (2023)
- [Pretrained Language Models for Text Generation: A Survey](https://mlsanity.com/paper/2105.10311/) (2021)
- [Fighting an Infodemic: COVID-19 Fake News Dataset](https://mlsanity.com/paper/2011.03327/) (2020)
- [A Primer on Contrastive Pretraining in Language Processing: Methods, Lessons Learned and Perspectives](https://mlsanity.com/paper/2102.12982/) (2021)
- [FakeSV: A Multimodal Benchmark with Rich Social Context for Fake News Detection on Short Video Platforms](https://mlsanity.com/paper/2211.10973/) (2022)
- [Dbias: Detecting biases and ensuring Fairness in news articles](https://mlsanity.com/paper/2208.05777/) (2022)
- [Some Like it Hoax: Automated Fake News Detection in Social Networks](https://mlsanity.com/paper/1704.07506/) (2017)
- [Exploring Faithful Rationale for Multi-hop Fact Verification via Salience-Aware Graph Learning](https://mlsanity.com/paper/2212.01060/) (2022)
- [A framework for fake review detection in online consumer electronics retailers](https://mlsanity.com/paper/1903.12452/) (2019)
- [CHECKED: Chinese COVID-19 Fake News Dataset](https://mlsanity.com/paper/2010.09029/) (2020)

## Built on by (most cited first, 4 of 4)

- [DELL: Generating Reactions and Explanations for LLM-Based Misinformation Detection](https://mlsanity.com/paper/2402.10426/) (2024)
- [What Does the Bot Say? Opportunities and Risks of Large Language Models in Social Media Bot Detection](https://mlsanity.com/paper/2402.00371/) (2024)
- [Generative Large Language Models in Automated Fact-Checking: A Survey](https://mlsanity.com/paper/2407.02351/) (2024)
- [AEScorer: An Agentic Evidence-Grounded Framework for Graded Factuality Verification](https://mlsanity.com/paper/2601.03605/) (2026)

## Abstract (arXiv)

The proliferation of fake news has emerged as a critical issue in recent years, requiring significant efforts to detect it. However, the existing fake news detection datasets are sourced from human journalists, which are likely to have inherent bias limitations due to the highly subjective nature of this task. In this paper, we revisit the existing fake news dataset verified by human journalists with augmented fact-checking by large language models (ChatGPT), and we name the augmented fake news dataset ChatGPT-FC. We quantitatively analyze the distinctions and resemblances between human journalists and LLM in assessing news subject credibility, news creator credibility, time-sensitive, and political framing. Our findings highlight LLM's potential to serve as a preliminary screening method, offering a promising avenue to mitigate the inherent biases of human journalists and enhance fake news detection.