Identifying how COVID-19-related misinformation reacts to the announcement of the UK national lockdown: An interrupted time-series study

Mark Green*, Elena Musi, Francisco Rowe, Darren Charles, Frances Darlington Pollock, Chris Kypridemos, Andrew Morse, Patricia Rossini, John Tulloch, Andrew Davies, Emily Dearden, Henrdramoorthy Maheswaran, Alex Singleton, Roberto Vivancos, Sally Sheard

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

COVID-19 is unique in that it is the first global pandemic occurring amidst a crowded information environment that has facilitated the proliferation of misinformation on social media. Dangerous misleading narratives have the potential to disrupt ‘official’ information sharing at major government announcements. Using an interrupted time-series design, we test the impact of the announcement of the first UK lockdown (8–8.30 p.m. 23 March 2020) on short-term trends of misinformation on Twitter. We utilise a novel dataset of all COVID-19-related social media posts on Twitter from the UK 48 hours before and 48 hours after the announcement (n = 2,531,888). We find that while the number of tweets increased immediately post announcement, there was no evidence of an increase in misinformation-related tweets. We found an increase in COVID-19-related bot activity post-announcement. Topic modelling of misinformation tweets revealed four distinct clusters: ‘government and policy’, ‘symptoms’, ‘pushing back against misinformation’ and ‘cures and treatments’.

Original languageEnglish
JournalBig Data and Society
Volume8
Issue number1
DOIs
Publication statusPublished - 2021

Bibliographical note

Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The project was funded by the National Institute for Health Research Health Protection Research Unit in Emerging and Zoonotic Infections, the Centre of Excellence in Infectious Diseases Research and the Alder Hey Charity. The authors also received the support from Liverpool Health Partners and the Liverpool-Malawi-Covid-19 Consortium. The work was also supported by the Economic and Social Research Council (grant number ES/L011840/1). Darren Charles is funded by the National Institute for Health Research Applied Research Collaboration North West Coast. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health Research or the Department of Health and Social Care.

Publisher Copyright:
© The Author(s) 2021.

Keywords

  • bots
  • COVID-19
  • Misinformation
  • social media
  • Twitter

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