ReCOVery: COVID-19 News Credibility Repository
A ReCOVery (news credibility repository) dataset of 2,029 news articles with reliability labels and social media engagement, collected by Zhou, Mulay, Ferrara, and Zafarani via news websites and Twitter (2020). Each record contains news article title and body, reliability label (1=reliable, 0=unreliable), publisher, political bias, country, author, and social media engagement data. The dataset is notable for its multimodal repository combining news content with social media engagement, designed for COVID-19 news credibility research. Intended for research into fake news detection, credibility assessment, political bias analysis, and multimodal misinformation research.
Cite this dataset at
recovery.echo.directory
Analytics
The full graph has 142,849 nodes; this view shows the 1,000-node sample the pipeline drew for display. Node size is centrality, colour is community.
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Computed over the full corpus.
Record
- Catalog key
recovery- Authors
- Zhou, Xinyi; Mulay, Apurva; Ferrara, Emilio; Zafarani, Reza
- Year
- 2020
- Source paper
- ReCOVery: A Multimodal Repository for COVID-19 News Credibility Research
- DOI / arXiv
- 10.1145/3340531.3412880
- License
- Academic use (see GitHub) (LicenseRef-Research-Only)
- Records
- 142,849
- Nodes
- 142,849
- Edges
- 140,820 (directed)
- Nodes are
- article / tweet
- Edges are
- article-tweet link
- Attributes
- nodes have attributes; edges have attributes
- Text corpus
- news article text
- Languages
- en
- Collected
- 2020 (2020s)
- Tasks
- From the graph: link prediction, community detection
- Region
- Global (inferred: the platform is used worldwide)
Provenance
- Originally published at
- https://github.com/apurvamulay/ReCOVery link checked
- Hosted copy
- Mirrored on echo.directory on 2026-10-09, 14.1 MB across 2 files. Checksums in MANIFEST.json.
Citations
ReCOVery: A Multimodal Repository for COVID-19 News Credibility Research
Xinyi Zhou, Apurva Mulay, Emilio Ferrara, R. Zafarani
Journal Article · International Conference on Information and Knowledge Management · 2020
Cited by
- Chaos, Solitons and Fractals
Nonlinear Science, Complex Phenomena et al. · cited 677× - Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society
Firoj Alam, Shaden Shaar et al. · Conference on Empirical Methods in Natural Language Processing · 2020 · cited 188× - Characterizing COVID-19 Misinformation Communities Using a Novel Twitter Dataset
Shahan Ali Memon, Kathleen M. Carley · International Conference on Information and Knowledge Management · 2020 · cited 185× - Deep learning for fake news detection: A comprehensive survey
Linmei Hu, Siqi Wei et al. · AI Open · 2022 · cited 175× - Applications of artificial intelligence in battling against covid-19: A literature review
Mohammad-H. Tayarani N. · Chaos, Solitons & Fractals · 2020 · cited 166×
Show all 10
- ANTi-Vax: a novel Twitter dataset for COVID-19 vaccine misinformation detection
Kadhim Hayawi, Sakib Shahriar et al. · Public Health · 2021 · cited 157× - Multimodal fake news detection through data augmentation-based contrastive learning
Jiaheng Hua, Xiao-Dong Cui et al. · Applied Soft Computing · 2023 · cited 145× - Memory-Guided Multi-View Multi-Domain Fake News Detection
Yongchun Zhu, Qiang Sheng et al. · IEEE Transactions on Knowledge and Data Engineering · 2022 · cited 144× - COVID-19 on Social Media: Analyzing Misinformation in Twitter Conversations
Karishma Sharma, Sungyong Seo et al. · 2020 · cited 136× - Fighting the COVID-19 Infodemic in Social Media: A Holistic Perspective and a Call to Arms
Firoj Alam, Fahim Dalvi et al. · International Conference on Web and Social Media · 2020 · cited 116×
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}
}
@inproceedings{Zhou_2020,
series = {CIKM ’20},
title = {ReCOVery: A Multimodal Repository for COVID-19 News Credibility Research},
url = {http://dx.doi.org/10.1145/3340531.3412880},
doi = {10.1145/3340531.3412880},
booktitle = {Proceedings of the 29th ACM International Conference on Information & Knowledge Management},
publisher = {ACM},
author = {Zhou, Xinyi and Mulay, Apurva and Ferrara, Emilio and Zafarani, Reza},
year = {2020},
month = Oct,
pages = {3205–3212},
collection = {CIKM ’20}
} APA
Switzer, B., & Zafarani, R. (2026). ECHO: Exploratory Compendium of Humanity Online [Data repository]. Syracuse University. https://echo.directory
Zhou, X., Mulay, A., Ferrara, E., & Zafarani, R. (2020). ReCOVery: COVID-19 News Credibility Repository [Data set]. https://doi.org/10.1145/3340531.3412880
MLA
Switzer, Braden, and Reza Zafarani. ECHO: Exploratory Compendium of Humanity Online. Syracuse University, 2026, echo.directory.
Zhou, Xinyi, et al. ReCOVery: COVID-19 News Credibility Repository. 2020, doi.org/10.1145/3340531.3412880.