Identifying how COVID-19-related misinformation reacts to the announcement of the UK national lockdown: An interrupted time-series study. Issue 1 (May 2021)

• Record Type: Journal Article
• Authors: Green, Mark; Musi, Elena; Rowe, Francisco; Charles, Darren; Pollock, Frances Darlington; Kypridemos, Chris; Morse, Andrew; Rossini, Patricia; Tulloch, John; Davies, Andrew; Dearden, Emily; Maheswaran, Henrdramoorthy; Singleton, Alex; Vivancos, Roberto; Sheard, Sally
• Is Part Of: Big data & society. Volume 8:Issue 1(2021)
• Publication Date: 2021-05
• Subjects: Misinformation -- social media -- Twitter -- COVID-19 -- bots; Big data -- Social aspects -- Periodicals; Social sciences -- Research -- Data processing -- Periodicals; Social sciences -- Research -- Methodology -- Periodicals; Data mining -- Periodicals; 300.28557
• DOI: 10.1177/20539517211013869
• Languages: English
• ISSNs: 2053-9517

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'.