COVID-19 Misinformation Videos Dataset
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YouTubeContent moderationCovidHealthMisinformationVideo & streaming A YouTube (with cross-platform spread on Twitter and Facebook) dataset of 7,659 removed YouTube videos with multilingual titles and descriptions, 249,346 associated tweets, and 18,897,277 Facebook shares, collected by Knuutila (2021) via YouTube Data API and CrowdTangle, tracking videos shared on public social media that were subsequently removed by YouTube for COVID-19 misinformation. Each record contains video titles and descriptions in multiple languages, view counts and subscriber counts where recoverable, Facebook reactions/comments/shares, Twitter and Facebook post ID lists for social spread tracking, removal and publication timestamps, and Wayback Machine archive URLs. The dataset is notable for its one of the few public datasets documenting the complete lifecycle of platform-removed health misinformation, linking video metadata to cross-platform social spread and enabling research into both content and dissemination patterns of COVID-19 misinformation. Intended for research into health misinformation detection, content moderation research, cross-platform information spread analysis, COVID-19 infodemic studies, multilingual misinformation narrative analysis, and platform policy impact assessment.
Cite this dataset at
covid-misinfo-videos.echo.directory
Analytics
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Computed over the full corpus.
Record
- Catalog key
covid-misinfo-videos- Authors
- Knuutila, Aleksi; Herasimenka, Aliaksandr; Au, Hubert; Bright, Jonathan; Howard, Philip N.
- Year
- 2021
- Source paper
- A Dataset of COVID-Related Misinformation Videos and their Spread on Social Media
- DOI / arXiv
- 10.5334/johd.24
- License
- Creative Commons Attribution 4.0 International (CC BY 4.0) (CC-BY-4.0)
- Records
- 13,820
- Languages
- de, en, es, fr, it, pt
- Collected
- November 2019 to June 2020 (2010s, 2020s)
- Region
- Global (inferred: the platform is used worldwide)
Provenance
- Originally published at
- https://zenodo.org/records/4557828 link checked
- Hosted copy
- Mirrored on echo.directory on 2026-10-02, 8.7 MB across 1 files. Checksums in MANIFEST.json.
Citations
A Dataset of COVID-Related Misinformation Videos and their Spread on Social Media
Aleksi Knuutila, Aliaksandr Herasimenka, Hubert Au, Jonathan Bright, P. Howard
Journal of Open Humanities Data · 2021
Cited by
- One Year of COVID-19 Vaccine Misinformation on Twitter: Longitudinal Study
F. Pierri, Matthew R. Deverna et al. · Journal of Medical Internet Research · 2022 · cited 81× - Social media mining under the COVID-19 context: Progress, challenges, and opportunities
Xiao Huang, Si-Qin Wang et al. · International Journal of Applied Earth Observation and Geoinformation · 2022 · cited 69× - Deep Impact: A Study on the Impact of Data Papers and Datasets in the Humanities and Social Sciences
Barbara McGillivray, P. Marongiu et al. · Publ. · 2022 · cited 28× - Evaluating YouTube TM as a patient information source for the Risks of Root Canal Treatment.
S. McLean, N. Cook et al. · Journal of Endodontics · 2022 · cited 17× - Harmful YouTube Video Detection: A Taxonomy of Online Harm and MLLMs as Alternative Annotators
C. Jo, Miki Wesołowska et al. · Computational Communication Research · 2024 · cited 15×
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- FactGuard: Agentic Video Misinformation Detection via Reinforcement Learning
Zehao Li, Hong-Wei Yu et al. · arXiv.org · 2026 · cited 7× - Unveiling misinformation on YouTube: examining the content of COVID-19 vaccination misinformation videos in Switzerland
Edda Humprecht, S. Kessler · Frontiers in Communication · 2024 · cited 7× - MetaHarm: Harmful YouTube Video Dataset Annotated by Domain Experts, GPT-4-Turbo, and Crowdworkers
C. Jo, Magdalena Wojcieszak · International Conference on Web and Social Media · 2025 · cited 3× - Generalising Across Domains in Video Misinformation Detection
David Walker, Seani Rananga et al. · 2024 IST-Africa Conference (IST-Africa) · 2024 · cited 1× - Understanding the Effects of Visual Misinformation: A Systematic Review of 10 Years (2014–2024)
Anita K. C. Liu, Ozan Kuru · Mass Communication & Society · 2025 · cited 0×
Citation data from Semantic Scholar, retrieved 2026-10-22.
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{Knuutila_2021,
title = {A Dataset of COVID-Related Misinformation Videos and their Spread on Social Media},
volume = {7},
issn = {2059-481X},
url = {http://dx.doi.org/10.5334/johd.24},
doi = {10.5334/johd.24},
journal = {Journal of Open Humanities Data},
publisher = {Ubiquity Press, Ltd.},
author = {Knuutila, Aleksi and Herasimenka, Aliaksandr and Au, Hubert and Bright, Jonathan and Howard, Philip N.},
year = {2021}
} APA
Switzer, B., & Zafarani, R. (2026). ECHO: Exploratory Compendium of Humanity Online [Data repository]. Syracuse University. https://echo.directory
Knuutila, A., Herasimenka, A., Au, H., Bright, J., & Howard, P. N. (2021). COVID-19 Misinformation Videos Dataset [Data set]. https://doi.org/10.5334/johd.24
MLA
Switzer, Braden, and Reza Zafarani. ECHO: Exploratory Compendium of Humanity Online. Syracuse University, 2026, echo.directory.
Knuutila, Aleksi, et al. COVID-19 Misinformation Videos Dataset. 2021, doi.org/10.5334/johd.24.