Abstract

Background
The novel coronavirus, also known as SARS-CoV-2, has come to define much of our lives since the beginning of 2020. During this time, countries around the world imposed lockdowns and social distancing measures. The physical movements of people ground to a halt, while their online interactions increased as they turned to engaging with each other virtually. As the means of communication shifted online, information consumption also shifted online. Governing authorities and health agencies have intentionally shifted their focus to use social media and online platforms to spread factual and timely information. However, this has also opened the gate for misinformation, contributing to and accelerating the phenomenon of misinfodemics.
Objective
We carried out an analysis of Twitter discourse on over 1 billion tweets related to COVID-19 over a year to identify and investigate prevalent misinformation narratives and trends. We also aimed to describe the Twitter audience that is more susceptible to health-related misinformation and the network mechanisms driving misinfodemics.
Methods
We leveraged a data set that we collected and made public, which contained over 1 billion tweets related to COVID-19 between January 2020 and April 2021. We created a subset of this larger data set by isolating tweets that included URLs with domains that had been identified by Media Bias/Fact Check as being prone to questionable and misinformation content. By leveraging clustering and topic modeling techniques, we identified major narratives, including health misinformation and …

Metadata

publication

JMIR Infodemiology 2 (1), e32378, 2022

year

2022

publication date

2022/2/8

authors

Emily Chen, Julie Jiang, Ho-Chun Herbert Chang, Goran Muric, Emilio Ferrara

link

https://infodemiology.jmir.org/2022/1/e32378

resource_link

https://infodemiology.jmir.org/2022/1/e32378

journal

JMIR Infodemiology

volume

2

issue

1

pages

e32378

publisher

JMIR Publications