Context

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, this dataset created.

Content

The datasets is a diverse COVID-19 healthcare misinformation dataset, including fake news on websites and social platforms, along with users' social engagement about such news. It includes 4,251 news, 296,000 related user engagements, 926 social platform posts about COVID-19, and ground truth labels.

• Version 0.1 (05/17/2020)
  initial version corresponding to arXiv paper
  CoAID: COVID-19 HEALTHCARE MISINFORMATION DATASET

• Version 0.2 (08/03/2020)
  added data from May 1, 2020 through July 1, 2020

• Version 0.3 (11/03/2020)
  added data from July 1, 2020 through September 1, 2020

Acknowledgements

Limeng Cui Dongwon Lee, Pennsylvania State University.