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Accepted for/Published in: Journal of Medical Internet Research

Date Submitted: Jun 14, 2020
Date Accepted: Aug 6, 2020
Date Submitted to PubMed: Aug 13, 2020

The final, peer-reviewed published version of this preprint can be found here:

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

Doogan C, Buntine W, Linger H, Brunt S

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

J Med Internet Res 2020;22(9):e21419

DOI: 10.2196/21419

PMID: 32784190

PMCID: 7505256

Public Perceptions and Attitudes Towards COVID-19 Non-Pharmaceutical Interventions Across Six Countries: A Topic Modeling Analysis of Twitter Data

  • Caitlin Doogan; 
  • Wray Buntine; 
  • Henry Linger; 
  • Samantha Brunt

ABSTRACT

Background:

Non-pharmaceutical interventions (NPIs) 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 towards NPI regimes and the factors that influence these. Twitter data offers 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 towards NPIs across these countries. The aim is to identify factors that influenced the 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 [UK], and the United States [US]). 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 dataset displayed the greatest attention to NPIs, and the US dataset 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 relating 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 between 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 would 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.


 Citation

Please cite as:

Doogan C, Buntine W, Linger H, Brunt S

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

J Med Internet Res 2020;22(9):e21419

DOI: 10.2196/21419

PMID: 32784190

PMCID: 7505256

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