Year: 2020
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Authors: Cui, Limeng, Lee, Dongwon
Institution: Pennsylvania State University
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
Funder: National Science Foundation
Awards: Developing and Evaluating Fraud Informatics Curriculum among Institutions in the Appalachian Region, Vertical Search Engine and Graph Homomorphism for Enhancing the Cybersecurity Workforce, EAGER: Training Computers and Humans to Detect Misinformation by Combining Computational and Theoretical Analysis, EAGER: SaTC: Early-Stage Interdisciplinary Collaboration: Modeling Memory Illusion for Predicting Trust in Online Information
OpenAlex ID: W3032089915