CHECKED: Chinese COVID-19 Fake News Dataset
A Weibo (CHECKED dataset) dataset of 2,104 microblogs with engagement and expert analysis, collected by Yang, Zhou, and Zafarani via Weibo (2020-2021). Each record contains microblog text, images, videos, engagement metrics (comments, reposts, likes), and expert analysis for fake news. The dataset is notable for its first Chinese COVID-19 fake news dataset with expert analysis, anonymized user IDs, and rich social interaction data. Intended for research into fake news detection, misinformation analysis, Chinese NLP, social network analysis, and public health communication.
Cite this dataset at
checked.echo.directory
Analytics
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Computed over the full corpus.
Record
- Catalog key
checked- Authors
- Yang, Chen; Zhou, Xinyi; Zafarani, Reza
- Year
- 2021
- DOI / arXiv
- 10.1007/s13278-021-00766-8
- License
- Academic use only (see GitHub) (LicenseRef-Research-Only)
- Records
- 2,104
- Languages
- zh
- Collected
- 2020-2021 (2020s)
- Tasks
- Fake news detection (binary;
label) inferred - Region
- China · East Asia (inferred: the platform is used mostly there)
Provenance
- Originally published at
- https://github.com/cyang03/CHECKED link checked
- Hosted copy
- Mirrored on echo.directory on 2026-10-09, 197.1 MB across 6 files. Checksums in MANIFEST.json.
Citations
CHECKED: Chinese COVID-19 fake news dataset
Chen Yang, Xinyi Zhou, R. Zafarani
Journal Article · Social Network Analysis and Mining · 2020
Cited by
- Deep learning for fake news detection: A comprehensive survey
Linmei Hu, Siqi Wei et al. · AI Open · 2022 · cited 175× - ANTi-Vax: a novel Twitter dataset for COVID-19 vaccine misinformation detection
Kadhim Hayawi, Sakib Shahriar et al. · Public Health · 2021 · cited 157× - Infodemic and fake news – A comprehensive overview of its global magnitude during the COVID-19 pandemic in 2021: A scoping review
V. Balakrishnan, W. Ng et al. · International Journal of Disaster Risk Reduction · 2022 · cited 119× - g2tmn at Constraint@AAAI2021: Exploiting CT-BERT and Ensembling Learning for COVID-19 Fake News Detection
Anna Glazkova, Maksim Glazkov et al. · CONSTRAINT@AAAI · 2020 · cited 71× - DC-CNN: Dual-channel Convolutional Neural Networks with attention-pooling for fake news detection
Kun Ma, Changhao Tang et al. · Applied intelligence (Boston) · 2022 · cited 55×
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- Cross-lingual COVID-19 Fake News Detection
Jiang-Shu Du, Yingtong Dou et al. · 2021 International Conference on Data Mining Workshops (ICDMW) · 2021 · cited 49× - Dataset of Fake News Detection and Fact Verification: A Survey
Taichi Murayama · arXiv.org · 2021 · cited 47× - MCFEND: A Multi-source Benchmark Dataset for Chinese Fake News Detection
Yu-Peng Li, Haorui He et al. · The Web Conference · 2024 · cited 42× - A systematic literature review and existing challenges toward fake news detection models
Minal Nirav Shah, A. Ganatra · Social Network Analysis and Mining · 2022 · cited 40× - COVID-19 fake news detection: A hybrid CNN-BiLSTM-AM model
Huo-Song Xia, Yuan Wang et al. · Technological forecasting & social change · 2023 · cited 39×
Citation data from Semantic Scholar, retrieved 2026-10-19.
How to cite
BibTeX
@misc{echo2026,
title = {ECHO: Exploratory Compendium of Humanity Online},
author = {Switzer, Braden and Zafarani, Reza},
year = {2026},
url = {https://echo.directory},
note = {Syracuse University}
}
@article{yang2021checked,
title = {{CHECKED}: Chinese {COVID-19} Fake News Dataset},
author = {Yang, Chen and Zhou, Xinyi and Zafarani, Reza},
journal = {Social Network Analysis and Mining},
year = {2021},
doi = {10.1007/s13278-021-00766-8}
} APA
Switzer, B., & Zafarani, R. (2026). ECHO: Exploratory Compendium of Humanity Online [Data repository]. Syracuse University. https://echo.directory
Yang, C., Zhou, X., & Zafarani, R. (2021). CHECKED: Chinese COVID-19 Fake News Dataset [Data set]. https://doi.org/10.1007/s13278-021-00766-8
MLA
Switzer, Braden, and Reza Zafarani. ECHO: Exploratory Compendium of Humanity Online. Syracuse University, 2026, echo.directory.
Yang, Chen, et al. CHECKED: Chinese COVID-19 Fake News Dataset. 2021, doi.org/10.1007/s13278-021-00766-8.