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

doi:10.1177/20539517211013869

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

Field Epidemiology (North West)

Research output: Contribution to journal › Article › peer-review

11 Citations (Scopus)

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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 language	English
Journal	Big Data and Society
Volume	8
Issue number	1
Early online date	9 May 2021
DOIs	https://doi.org/10.1177/20539517211013869
Publication status	Published - Jun 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.

Open Access: Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage

Publisher Copyright: © The Author(s) 2021.

Citation: Green, M., Musi, E., Rowe, F., Charles, D., Pollock, F. D., Kypridemos, C., Morse, A., Rossini, P., Tulloch, J., Davies, A., Dearden, E., Maheswaran, H., Singleton, A., Vivancos, R., & Sheard, S. (2021). Identifying how COVID-19-related misinformation reacts to the announcement of the UK national lockdown: An interrupted time-series study. Big Data & Society, 8(1). https://doi.org/10.1177/20539517211013869

DOI: https://doi.org/10.1177/20539517211013869

Keywords

COVID-19
Misinformation
Twitter
bots
social media

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10.1177/20539517211013869Licence: CC BY

Green2021IdentifyingHowCOVID-19-relatedMisinformationReactsToTheAnnouncementOfTheUKNationalLockdownBigDataSocFinal published version, 753 KBLicence: CC BY

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Green, M., Musi, E., Rowe, F., Charles, D., Pollock, F. D., Kypridemos, C., Morse, A., Rossini, P., Tulloch, J., Davies, A., Dearden, E., Maheswaran, H., Singleton, A., Vivancos, R., & Sheard, S. (2021). Identifying how COVID-19-related misinformation reacts to the announcement of the UK national lockdown: An interrupted time-series study. Big Data and Society, 8(1). https://doi.org/10.1177/20539517211013869

@article{71cd7a82b9664e6a8762a08e19abd458,

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

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 {\textquoteleft}official{\textquoteright} 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: {\textquoteleft}government and policy{\textquoteright}, {\textquoteleft}symptoms{\textquoteright}, {\textquoteleft}pushing back against misinformation{\textquoteright} and {\textquoteleft}cures and treatments{\textquoteright}.",

keywords = "COVID-19, Misinformation, Twitter, bots, social media",

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

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. Open Access: Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage Publisher Copyright: {\textcopyright} The Author(s) 2021. Citation: Green, M., Musi, E., Rowe, F., Charles, D., Pollock, F. D., Kypridemos, C., Morse, A., Rossini, P., Tulloch, J., Davies, A., Dearden, E., Maheswaran, H., Singleton, A., Vivancos, R., & Sheard, S. (2021). Identifying how COVID-19-related misinformation reacts to the announcement of the UK national lockdown: An interrupted time-series study. Big Data & Society, 8(1). https://doi.org/10.1177/20539517211013869 DOI: https://doi.org/10.1177/20539517211013869",

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Green, M, Musi, E, Rowe, F, Charles, D, Pollock, FD, Kypridemos, C, Morse, A, Rossini, P, Tulloch, J, Davies, A, Dearden, E, Maheswaran, H, Singleton, A, Vivancos, R & Sheard, S 2021, 'Identifying how COVID-19-related misinformation reacts to the announcement of the UK national lockdown: An interrupted time-series study', Big Data and Society, vol. 8, no. 1. https://doi.org/10.1177/20539517211013869

TY - JOUR

T1 - Identifying how COVID-19-related misinformation reacts to the announcement of the UK national lockdown

T2 - An interrupted time-series study

AU - Green, Mark

AU - Musi, Elena

AU - Rowe, Francisco

AU - Charles, Darren

AU - Pollock, Frances Darlington

AU - Kypridemos, Chris

AU - Morse, Andrew

AU - Rossini, Patricia

AU - Tulloch, John

AU - Davies, Andrew

AU - Dearden, Emily

AU - Maheswaran, Henrdramoorthy

AU - Singleton, Alex

AU - Vivancos, Roberto

AU - Sheard, Sally

N1 - 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. Open Access: Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage Publisher Copyright: © The Author(s) 2021. Citation: Green, M., Musi, E., Rowe, F., Charles, D., Pollock, F. D., Kypridemos, C., Morse, A., Rossini, P., Tulloch, J., Davies, A., Dearden, E., Maheswaran, H., Singleton, A., Vivancos, R., & Sheard, S. (2021). Identifying how COVID-19-related misinformation reacts to the announcement of the UK national lockdown: An interrupted time-series study. Big Data & Society, 8(1). https://doi.org/10.1177/20539517211013869 DOI: https://doi.org/10.1177/20539517211013869

PY - 2021/6

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N2 - 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’.

AB - 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’.

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

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Keywords

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