JMIR Publications

ABSTRACT

Background:

The novel coronavirus, also known as COVID-19 or 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; our physical movements ground to a halt, while our online interactions increased as we turned to engaging with each other virtually. As our means of communication shifted online, so too did information consumption. While there has been an intentional shift and focus by governing authorities and health agencies on using social media and online platforms to spread factual and timely information, this has also opened the gate for misinformation, contributing to the phenomenon of misinfodemics.

Objective:

In this paper, we carry out an over a year-long analysis of Twitter discourse on over a billion tweets related to COVID-19 to identify and investigate prevalent misinformation narratives and trends. We also aim to describe the Twitter audience that is more susceptible to health-related misinformation and the network mechanisms driving misinfodemics.

Methods:

We leverage a dataset that we collected, and made public, containing over one billion tweets related to COVID-19 spanning between January 2020 and April 2021. We create a subset of this larger dataset by isolating tweets that include URLs with domains that have been identified by Media Bias/Fact Check as being prone to questionable and misinformation content. By leveraging clustering and topic modeling techniques, we identify the major narratives, including health misinformation and conspiracies, that are present within this subset of tweets.

Results:

Our focus is on a subset of 12,689,165 tweets that we determined are representative of COVID-19 misinformation narratives in our full dataset. When analyzing tweets that share content from domains known to be questionable or that promote misinformation, we find that a few key misinformation narratives emerge about Hydroxychloroquine and alternative medicines, United States officials and governing agencies directives, and COVID-19 prevention measures. We further analyze the misinformation retweet network and find that users who share both questionable and conspiracy-related content are clustered more closely in the network than others, supporting the hypothesis that echo chambers can contribute to the spread of health misinfodemics.

Conclusions:

Our paper presents a summary and analysis of the major misinformation discourse surrounding COVID-19 and those who promoted and engaged with it. While misinformation is not limited to social media platforms, we hope that our insights will shed light on how best to combat misinformation, particularly pertaining to health-related emergencies, and pave the way for computational infodemiology to inform health surveillance and interventions.