Studying the COVID-19 infodemic at scale-Reference-Cited by-同舟云学术

Studying the COVID-19 infodemic at scale

Published:2021-01 Issue:1 Volume:8 Page:205395172110211
ISSN:2053-9517
Container-title:Big Data & Society
language:en
Short-container-title:Big Data & Society

Author:

Gruzd Anatoliy¹^ORCID,De Domenico Manlio²,Sacco Pier Luigi³⁴^ORCID,Briand Sylvie⁵

Affiliation:

1. Ted Rogers School of Management, Ryerson University, Toronto, Canada

2. CoMuNe Lab, Fondazione Bruno Kessler, Povo (TN), Italy

3. Department of Humanities, IULM University, Milan, Italy

4. metaLAB (at) Harvard, Cambridge, MA, USA

5. Global Infectious Hazards Preparedness Emergency Preparedness, World Health Organization, Geneva, Switzerland

Abstract

This special theme issue of Big Data & Society presents leading-edge, interdisciplinary research that focuses on examining how health-related (mis-)information is circulating on social media. In particular, we are focusing on how computational and Big Data approaches can help to provide a better understanding of the ongoing COVID-19 infodemic (overexposure to both accurate and misleading information on a health topic) and to develop effective strategies to combat it.

Funder

Canadian Institutes of Health Research

Publisher

SAGE Publications

Subject

Library and Information Sciences,Information Systems and Management,Computer Science Applications,Communication,Information Systems

Link

http://journals.sagepub.com/doi/pdf/10.1177/20539517211021115

Reference24 articles.

1. Examining algorithmic biases in YouTube’s recommendations of vaccine videos

2. The case for tracking misinformation the way we track disease

3. Managing the infodemic

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