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

Mark Green¹, Elena Musi², Francisco Rowe¹, Darren Charles³, Frances Darlington Pollock¹, Chris Kypridemos⁴, Andrew Morse¹, Patricia Rossini², John Tulloch^5,6, Andrew Davies¹, Emily Dearden^6,7, Henrdramoorthy Maheswaran⁸, Alex Singleton¹, Roberto Vivancos^5,6, Sally Sheard⁴.

¹ Department of Geography & Planning, University of Liverpool, Liverpool, UK

² Department of Communication & Media, University of Liverpool , Liverpool, UK

³ Institute of Population Health Sciences, University of Liverpool, Liverpool, UK

⁴ Department of Public Health & Policy, University of Liverpool, Liverpool, UK

⁵ NIHR Heath Protection Research Unit in Emerging and Zoonotic Infections, University of Liverpool, Liverpool, UK

⁶ Public Health England, UK

⁷ Greater Manchester Combined Authority, Manchester, UK

⁸ Institute of Global Health Innovation, Imperial College London, London, UK.

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 (8-8.30pm 23rd 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. Following an increase in COVID-19-related bot activity on the day of the announcement. Topic modelling of misinformation tweets revealed four distinct clusters: ‘government and policy’, ‘symptoms’, ‘pushing back against misinformation’ and ‘cures and treatments’.

Keywords

Misinformation; social media; twitter; COVID-19; bots.

Journal

Currently under review at Big Data & Society as part of their Special Issue Studying Infodemic at Scale.

Additional information

Repo contains all shareable data and analytical code for the paper.

Correspondence: mark.green@liverpool.ac.uk

fcorowe/misinformation_uk_lockdown_2020

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