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CoAID: COVID-19 Healthcare Misinformation Dataset

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arxiv 2006.00885 v3 pith:KCMGNXGO submitted 2020-05-22 cs.SI cs.CL

Limeng Cui , Dongwon Lee This is my paper · ORCID
classification cs.SIcs.CL
keywords covid-19misinformationcoaiddatasethealthcarenewssocialhealth
verification ladder T0 review T1 audit T2 compute T3 formal
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As the COVID-19 virus quickly spreads around the world, unfortunately, misinformation related to COVID-19 also gets created and spreads like wild fire. Such misinformation has caused confusion among people, disruptions in society, and even deadly consequences in health problems. To be able to understand, detect, and mitigate such COVID-19 misinformation, therefore, has not only deep intellectual values but also huge societal impacts. To help researchers combat COVID-19 health misinformation, therefore, we present CoAID (Covid-19 heAlthcare mIsinformation Dataset), with diverse COVID-19 healthcare misinformation, including fake news on websites and social platforms, along with users' social engagement about such news. CoAID includes 4,251 news, 296,000 related user engagements, 926 social platform posts about COVID-19, and ground truth labels. The dataset is available at: https://github.com/cuilimeng/CoAID.

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Forward citations

Cited by 5 Pith papers

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    Fine-tuned LLMs generate social media text that evades state-of-the-art detectors and human readers, dropping detection accuracy from up to 99.9% to near chance.

  2. Large Language Models in Misinformation Ecosystems: Misuse, Defense, and Vulnerability

    cs.CR 2026-07 conditional novelty 5.0 of 10

    A role-layer survey unifies LLM misuse, LLM-based defense, and LLM-centric verification vulnerabilities across content, social, evidence, and workflow layers, then lists three open challenges.

  3. Angry but Accurate: Detecting and Profiling the Counter-Misinformation Ecosystem on Twitter

    cs.SI 2026-07 conditional novelty 5.0 of 10

    Anti-misinformation COVID-19 tweets are modestly but consistently more angry, disgusted, and sad than pro-misinformation tweets and come from more established users.

  4. A Comprehensive Dataset for Human vs. AI Generated Text Detection

    cs.CL 2025-10 reject novelty 4.0 of 10

    A dataset of ~58k NYT articles plus AI rewrites from six LLMs, evaluated with a rewrite-distance baseline reaching 58.35% detection and 8.92% attribution accuracy.

  5. Analysing Health Misinformation with Advanced Centrality Metrics in Online Social Networks

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    The proposed centrality metrics are largely re-labelings of PageRank, Katz centrality, and randomly seeded in-degree weighting, with weak and partly unverifiable validation.

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