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      Public Perceptions and Attitudes Toward COVID-19 Nonpharmaceutical Interventions Across Six Countries: A Topic Modeling Analysis of Twitter Data

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          Abstract

          Background

          Nonpharmaceutical interventions (NPIs) (such as wearing masks and social distancing) have been implemented by governments around the world to slow the spread of COVID-19. To promote public adherence to these regimes, governments need to understand the public perceptions and attitudes toward NPI regimes and the factors that influence them. Twitter data offer a means to capture these insights.

          Objective

          The objective of this study is to identify tweets about COVID-19 NPIs in six countries and compare the trends in public perceptions and attitudes toward NPIs across these countries. The aim is to identify factors that influenced public perceptions and attitudes about NPI regimes during the early phases of the COVID-19 pandemic.

          Methods

          We analyzed 777,869 English language tweets about COVID-19 NPIs in six countries (Australia, Canada, New Zealand, Ireland, the United Kingdom, and the United States). The relationship between tweet frequencies and case numbers was assessed using a Pearson correlation analysis. Topic modeling was used to isolate tweets about NPIs. A comparative analysis of NPIs between countries was conducted.

          Results

          The proportion of NPI-related topics, relative to all topics, varied between countries. The New Zealand data set displayed the greatest attention to NPIs, and the US data set showed the lowest. The relationship between tweet frequencies and case numbers was statistically significant only for Australia ( r=0.837, P<.001) and New Zealand ( r=0.747, P<.001). Topic modeling produced 131 topics related to one of 22 NPIs, grouped into seven NPI categories: Personal Protection (n=15), Social Distancing (n=9), Testing and Tracing (n=10), Gathering Restrictions (n=18), Lockdown (n=42), Travel Restrictions (n=14), and Workplace Closures (n=23). While less restrictive NPIs gained widespread support, more restrictive NPIs were perceived differently across countries. Four characteristics of these regimes were seen to influence public adherence to NPIs: timeliness of implementation, NPI campaign strategies, inconsistent information, and enforcement strategies.

          Conclusions

          Twitter offers a means to obtain timely feedback about the public response to COVID-19 NPI regimes. Insights gained from this analysis can support government decision making, implementation, and communication strategies about NPI regimes, as well as encourage further discussion about the management of NPI programs for global health events, such as the COVID-19 pandemic.

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          Most cited references 25

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          Using reflexivity to optimize teamwork in qualitative research.

          Reflexivity is often described as an individual activity. The authors propose that reflexivity employed as a team activity, through the sharing of reflexive writing (accounts of personal agendas, hidden assumptions, and theoretical definitions) and group discussions about arising issues, can improve the productivity and functioning of qualitative teams and the rigor and quality of the research. The authors review the literature on teamwork, highlighting benefits and pitfalls, and define and discuss the role for reflexivity. They describe their own team and detail how they work together on a project investigating doctor-patient communication about prescribing. The authors present two reflexive tools they have used and show through examples how they have influenced the effectiveness of their team in terms of process, quality, and outcome.
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            A biterm topic model for short texts

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                Author and article information

                Contributors
                Journal
                J Med Internet Res
                J. Med. Internet Res
                JMIR
                Journal of Medical Internet Research
                JMIR Publications (Toronto, Canada )
                1439-4456
                1438-8871
                September 2020
                3 September 2020
                3 September 2020
                : 22
                : 9
                Affiliations
                [1 ] Department of Human Centred Computing Faculty of Information Technology Monash University Caulfield Australia
                [2 ] Department of Data Science and AI Faculty of Information Technology Monash University Clayton Australia
                [3 ] Royal Perth Hospital Perth Australia
                Author notes
                Corresponding Author: Caitlin Doogan caitlin.doogan@ 123456monash.edu
                Article
                v22i9e21419
                10.2196/21419
                7505256
                32784190
                ©Caitlin Doogan, Wray Buntine, Henry Linger, Samantha Brunt. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 29.08.2020.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.

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