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

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

doi:10.1177/20539517211013869

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’.

Highlights

The ‘infodemic’ pandemicPublic health crises throughout human history have frequently seen the diffusion of misleading or inaccurate information (Mawdsley, 2020)
Our study demonstrates a novel analysis of Big Data to evaluate whether the announcement of the UK national lockdown had an impact on trends in the sharing of misinformation on Twitter
While we found evidence that there were more COVID-19-related tweets following the policy announcement on national TV, we did not find consistent evidence suggesting it led to an overall increase of misinformation being shared on Twitter

Summary

Introduction

Public health crises throughout human history have frequently seen the diffusion of misleading or inaccurate information (Mawdsley, 2020). Misinformation – defined here as any news, rumour, source or information that is misleading, inaccurate or not true and shared without clear intent to cause harm (where intent to harm was intended, this is often defined as disinformation) – has previously been identified as a global public health threat (Larson, 2018). With some population groups increasingly using the internet as their go-to source for healthrelated information (Daraz et al, 2019; Ofcom, 2020), closer scrutiny and analysis of how national government announcements are received online, both proactively and reactively, is appropriate

Objectives

Results

Discussion

Conclusion