# CHECKED: Chinese COVID-19 Fake News Dataset

arXiv: https://arxiv.org/abs/2010.09029 · HTML: https://mlsanity.com/paper/2010.09029/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2020.

## Built on by (most cited first, 11 of 11)

- [MCFEND: A Multi-source Benchmark Dataset for Chinese Fake News Detection](https://mlsanity.com/paper/2403.09092/) (2024)
- [Dataset of Fake News Detection and Fact Verification: A Survey](https://mlsanity.com/paper/2111.03299/) (2021)
- [Cross-lingual COVID-19 Fake News Detection](https://mlsanity.com/paper/2110.06495/) (2021)
- [The Paradigm Shift: A Comprehensive Survey on Large Vision Language Models for Multimodal Fake News Detection](https://mlsanity.com/paper/2601.15316/) (2026)
- [A Revisit of Fake News Dataset with Augmented Fact-checking by ChatGPT](https://mlsanity.com/paper/2312.11870/) (2023)
- [A Scoping Review of Natural Language Processing in Addressing Medically Inaccurate Information: Errors, Misinformation, and Hallucination](https://mlsanity.com/paper/2505.00008/) (2025)
- [LiveFact: A Dynamic, Time-Aware Benchmark for LLM-Driven Fake News Detection](https://mlsanity.com/paper/2604.04815/) (2026)
- [Checkovid: A COVID-19 misinformation detection system on Twitter using network and content mining perspectives](https://mlsanity.com/paper/2107.09768/) (2021)
- [LTCR: Long-Text Chinese Rumor Detection Dataset](https://mlsanity.com/paper/2306.07201/) (2023)
- [CANDY: Benchmarking LLMs' Limitations and Assistive Potential in Chinese Misinformation Fact-Checking](https://mlsanity.com/paper/2509.03957/) (2025)
- [RedNote-Vibe: A Dataset for Capturing Temporal Dynamics of AI-Generated Text in Lifestyle Social Media](https://mlsanity.com/paper/2509.22055/) (2025)

## Abstract (arXiv)

COVID-19 has impacted all lives. To maintain social distancing and avoiding exposure, works and lives have gradually moved online. Under this trend, social media usage to obtain COVID-19 news has increased. Also, misinformation on COVID-19 is frequently spread on social media. In this work, we develop CHECKED, the first Chinese dataset on COVID-19 misinformation. CHECKED provides a total 2,104 verified microblogs related to COVID-19 from December 2019 to August 2020, identified by using a specific list of keywords. Correspondingly, CHECKED includes 1,868,175 reposts, 1,185,702 comments, and 56,852,736 likes that reveal how these verified microblogs are spread and reacted on Weibo. The dataset contains a rich set of multimedia information for each microblog including ground-truth label, textual, visual, temporal, and network information. Extensive experiments have been conducted to analyze CHECKED data and to provide benchmark results for well-established methods when predicting fake news using CHECKED. We hope that CHECKED can facilitate studies that target misinformation on coronavirus. The dataset is available at https://github.com/cyang03/CHECKED.