CHECKED: Chinese COVID-19 Fake News Dataset
Not on the map. Why?
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.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Oct 2020.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 3; 2022: 0; 2023: 2; 2024: 1; 2025: 3; 2026: 2.
11 papers.
- 2024MCFEND: A Multi-source Benchmark Dataset for Chinese Fake News Detectioncited by 12
- 2021Dataset of Fake News Detection and Fact Verification: A Surveycited by 11
- 2021Cross-lingual COVID-19 Fake News Detectioncited by 7
- 2026The Paradigm Shift: A Comprehensive Survey on Large Vision Language Models for Multimodal Fake News Detectioncited by 6
- 2023A Revisit of Fake News Dataset with Augmented Fact-checking by ChatGPTcited by 4
- 2025A Scoping Review of Natural Language Processing in Addressing Medically Inaccurate Information: Errors, Misinformation, and Hallucinationcited by 2
- 2026LiveFact: A Dynamic, Time-Aware Benchmark for LLM-Driven Fake News Detectioncited by 2
- 2021Checkovid: A COVID-19 misinformation detection system on Twitter using network and content mining perspectivescited by 1
- 2023LTCR: Long-Text Chinese Rumor Detection Datasetcited by 1
- 2025CANDY: Benchmarking LLMs' Limitations and Assistive Potential in Chinese Misinformation Fact-Checkingcited by 1
- 2025RedNote-Vibe: A Dataset for Capturing Temporal Dynamics of AI-Generated Text in Lifestyle Social Mediacited by 0
- 2026LiveFact: A Dynamic, Time-Aware Benchmark for LLM-Driven Fake News Detection6 Apr 2026
- 2026The Paradigm Shift: A Comprehensive Survey on Large Vision Language Models for Multimodal Fake News Detection16 Jan 2026
- 2025RedNote-Vibe: A Dataset for Capturing Temporal Dynamics of AI-Generated Text in Lifestyle Social Media26 Sep 2025
- 2025CANDY: Benchmarking LLMs' Limitations and Assistive Potential in Chinese Misinformation Fact-Checking4 Sep 2025
- 2025A Scoping Review of Natural Language Processing in Addressing Medically Inaccurate Information: Errors, Misinformation, and Hallucination16 Apr 2025
- 2024MCFEND: A Multi-source Benchmark Dataset for Chinese Fake News Detection14 Mar 2024
- 2023A Revisit of Fake News Dataset with Augmented Fact-checking by ChatGPT19 Dec 2023
- 2023LTCR: Long-Text Chinese Rumor Detection Dataset12 Jun 2023
- 2021Dataset of Fake News Detection and Fact Verification: A Survey5 Nov 2021
- 2021Cross-lingual COVID-19 Fake News Detection13 Oct 2021
- 2021Checkovid: A COVID-19 misinformation detection system on Twitter using network and content mining perspectives20 Jul 2021