FaCov: COVID-19 Viral News and Rumors Fact-Check Articles Dataset

Authors

  • Shakshi Sharma Institute of Computer Science, University of Tartu, Estonia
  • Ekanshi Agrawal Department of Computer Science and Information Systems, BITS Pilani - Hyderabad, India
  • Rajesh Sharma Institute of Computer Science, University of Tartu, Estonia
  • Anwitaman Datta School of Computer Science and Engineering, Nanyang Technological University, Singapore

Keywords:

Credibility of online content, Trust; reputation; recommendation systems, Text categorization; topic recognition; demographic/gender/age identification, Qualitative and quantitative studies of social media

Abstract

COVID-19, which was first detected in late 2019 in Wuhan, China, has spread to the rest of the world and is currently deemed a global pandemic. A flux of events triggered by a wide ranging set of factors such as virus mutations and waves of infections, imperfect medical and policy interventions, and vested interest driven political posturing all have created a continuous state of uncertainty and strife. In this verbile environment, misinformation and fake news thrive and propagate easily through the modern efficient all-pervading media and social media tools, resulting in an infodemic running its course in conjunction with the pandemic. In this work, we present a COVID-19 related dataset – FaCov – a compilation of fact-checking articles that examine and evaluate some of the most widely circulated rumors and claims concerning the coronavirus. We have collected articles from 13 very popular fact-checking sources, along with information about the articles and the vetted verity assigned to the claims being evaluated. We also share insights into the dataset to highlight and understand the major conversations and conflicts in narratives encompassing the pandemic.

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Published

2022-05-31

How to Cite

Sharma, S., Agrawal, E., Sharma, R., & Datta, A. (2022). FaCov: COVID-19 Viral News and Rumors Fact-Check Articles Dataset. Proceedings of the International AAAI Conference on Web and Social Media, 16(1), 1312-1321. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/19383