International Journal of Advanced Culture Technology

The International Promotion Agency of Culture Technology (국제문화기술진흥원)
권호 표지
DOI QR코드

DOI QR Code

Computational Analysis on Twitter Users' Attitudes towards COVID-19 Policy Intervention

  • Joohee Kim (Dept. of Public Administration, Ewha Womans Univ.) ;
  • Yoomi Kim (Dept. of Public Administration, Ewha Womans Univ.)
  • Received : 2023.09.06
  • Accepted : 2023.10.20
  • Published : 2023.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

During the initial period of the COVID-19 pandemic, governments around the world implemented non-pharmaceutical interventions. For these policy interventions to be effective, authorities engaged in the political discourse of legitimising their activity to generate positive public attitudes. To understand effective COVID-19 policy, this study investigates public attitudes in South Korea, the United Kingdom, and the United States and how they reflect different legitimisation of policy intervention. We adopt a big data approach to analyse public attitudes, drawing from public comments posted on Twitter during selected periods. We collect the number of tweets related to COVID-19 policy intervention and conduct a sentiment analysis using a deep learning method. Public attitudes and sentiments in the three countries show different patterns according to how policy interventions were implemented. Overall concern about policy intervention is higher in South Korea than in the other two countries. However, public sentiments in all three countries tend to improve following implementation of policy intervention. The findings suggest that governments can achieve policy effectiveness when consistent and transparent communication take place during the initial period of the pandemic. This study contributes to the existing literature by applying big data analysis to explain which policies engender positive public attitudes.

Keywords

References

  1. G. Capano, M. Howlett, D.S. Jarvis, M. Ramesh, and N. Goyal, "Mobilizing policy (in) capacity to fight COVID-19: Understanding variations in state responses," Policy and Society, Vol. 39, No. 3, pp. 285-308, 2020. https://doi.org/10.1080/14494035.2020.1787628
  2. D. Toshkov, B. Carroll, and K. Yesilkagit, "Government capacity, societal trust or party preferences: what accounts for the variety of national policy responses to the COVID-19 pandemic in Europe?," Journal of European Public Policy, pp. 1-20, 2021. https://doi.org/10.1080/13501763.2021.1928270
  3. T. Hale et al., "A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker)," Nature Human Behaviour, Vol. 5, No. 4, pp. 529-538, 2021. https://doi.org/10.1038/s41562-021-01079-8
  4. N. Imai et al., "Adoption and impact of non-pharmaceutical interventions for COVID-19," (in eng), Wellcome Open Res, Vol. 5, p. 59, 2020. https://doi.org/10.12688/wellcomeopenres.15808.1
  5. J.M. Brauner et al., "Inferring the effectiveness of government interventions against COVID-19," Science, Vol. 371, No. 6531, 2021. https://doi.org/10.1126/science.abd9338
  6. B.Y. An and S.-Y. Tang, "Lessons from COVID-19 responses in East Asia: Institutional infrastructure and enduring policy instruments," The American Review of Public Administration, Vol. 50, No. 6-7, pp. 790-800, 2020. https://doi.org/10.1177/0275074020943707
  7. A.R. Migone, "The influence of national policy characteristics on COVID-19 containment policies: A comparative analysis," Policy Design and Practice, Vol. 3, No. 3, pp. 259-276, 2020. https://doi.org/10.1080/25741292.2020.1804660
  8. B. Yan, B. Chen, L. Wu, X. Zhang, and H. Zhu, "Culture, Institution, and COVID-19 First-Response Policy: A Qualitative Comparative Analysis of Thirty-One Countries," Journal of Comparative Policy Analysis: Research and Practice, Vol. 23, No. 2, pp. 219-233, 2021. https://doi.org/10.1080/13876988.2021.1878458
  9. B. Yan, X. Zhang, L. Wu, H. Zhu, and B. Chen, "Why do countries respond differently to COVID-19? A comparative study of Sweden, China, France, and Japan," The American review of public administration, Vol. 50, No. 6-7, pp. 762-769, 2020. https://doi.org/10.1177/0275074020942445
  10. P. Cairney and A. Wellstead, "COVID-19: effective policymaking depends on trust in experts, politicians, and the public," Policy Design and Practice, Vol. 4, No. 1, pp. 1-14, 2021. https://doi.org/10.1080/25741292.2020.1837466
  11. N. Harring, S.C. Jagers, and A. Lofgren, "COVID-19: Large-scale collective action, government intervention, and the importance of trust," World Devel., Vol. 138, p. 105236, 2021. https://doi.org/10.1016/j.worlddev.2020.105236
  12. M.A. Nelson, "The timing and aggressiveness of early government response to COVID-19: Political systems, societal culture, and more," World Devel., Vol. 146, p. 105550, 2021. https://doi.org/10.1016/j.worlddev.2021.105550
  13. N. Haug et al., "Ranking the effectiveness of worldwide COVID-19 government interventions," Nature human behaviour, Vol. 4, No. 12, pp. 1303-1312, 2020. https://doi.org/10.1038/s41562-020-01009-0
  14. M. Pincombe, V. Reese, and C.B. Dolan, "The effectiveness of national-level containment and closure policies across income levels during the COVID-19 pandemic: an analysis of 113 countries," Health Policy and Planning, 2021. https://doi.org/10.1093/heapol/czab054
  15. O. Bargain and U. Aminjonov, "Trust and compliance to public health policies in times of COVID-19," J. Public Econ., Vol. 192, p. 104316, 2020. https://doi.org/10.1016/j.jpubeco.2020.104316
  16. B.M. Zimmermann, A. Fiske, S. McLennan, A. Sierawska, N. Hangel, and A. Buyx, "Motivations and Limits for COVID-19 Policy Compliance in Germany and Switzerland," International Journal of Health Policy and Management, 2021. https://doi.org/10.34172/ijhpm.2021.30
  17. C. Clark, A. Davila, M. Regis, and S. Kraus, "Predictors of COVID-19 voluntary compliance behaviors: An international investigation," Global transitions, Vol. 2, pp. 76-82, 2020. https://doi.org/10.1016/j.glt.2020.06.003
  18. B.N. Kantor and J. Kantor, "Non-pharmaceutical interventions for pandemic COVID-19: a cross-sectional investigation of US general public beliefs, attitudes, and actions," Frontiers in medicine, Vol. 7, p. 384, 2020. https://doi.org/10.3389/fmed.2020.00384
  19. F. Lalot, M.S. Heering, M. Rullo, G.A. Travaglino, and D. Abrams, "The dangers of distrustful complacency: Low concern and low political trust combine to undermine compliance with governmental restrictions in the emerging Covid-19 pandemic," Group Processes & Intergroup Relations, p. 1368430220967986, 2020. https://doi.org/10.1177/1368430220967986
  20. D. Wang, S. Marmo-Roman, K. Krase, and L. Phanord, "Compliance with preventative measures during the COVID-19 pandemic in the USA and Canada: Results from an online survey," Soc. Work Health Care, pp. 1-16, 2021. https://doi.org/10.1080/00981389.2020.1871157
  21. S. Altmann et al., "Acceptability of app-based contact tracing for COVID-19: Cross-country survey study," JMIR mHealth and uHealth: JMU, Vol. 8, No. 8, p. e19857, 2020. https://doi.org/10.2196/19857
  22. M. Toll and A. Li, "Vaccine sentiments and under-vaccination: Attitudes and behaviour around Measles, Mumps, and Rubella vaccine (MMR) in an Australian cohort," Vaccine, Vol. 39, No. 4, pp. 751-759, 2021. https://doi.org/10.1016/j.vaccine.2020.11.021
  23. A.D. Dubey, "Twitter Sentiment Analysis during COVID-19 Outbreak," Available at SSRN 3572023, 2020. https://dx.doi.org/10.2139/ssrn.3572023
  24. S. Olcer, Y. Yilmaz-Aslan, and P. Brzoska, "Lay perspectives on social distancing and other official recommendations and regulations in the time of COVID-19: a qualitative study of social media posts," BMC Public Health, Vol. 20, No. 1, pp. 1-9, 2020. https://doi.org/10.1186/s12889-020-09079-5
  25. R. Saito and S. Haruyama, "Estimating time-series changes in social sentiment@ Twitter in US metropolises during the COVID-19 pandemic," Journal of Computational Social Science, pp. 1-30, 2022. https://doi.org/10.1007/s42001-022-00186-4
  26. P.D. Davidson, T. Muniandy, and D. Karmegam, "Perception of COVID-19 vaccination among Indian Twitter users: computational approach," Journal of Computational Social Science, pp. 1-20, 2023. https://doi.org/10.1007/s42001-023-00203-0
  27. K. Sundell and T. Olsson, "Social Interventin Research," ed. Oxford Bibliographies, 2017.
  28. Nuffield Council on Bioethics, "Public health: Ethical issues," 2007.
  29. H. Seale et al., "Improving the impact of non-pharmaceutical interventions during COVID-19: examining the factors that influence engagement and the impact on individuals," BMC Infect. Dis., Vol. 20, No. 1, pp. 1-13, 2020. https://doi.org/10.1186/s12879-020-05340-9
  30. A. Viens, C.M. Bensimon, and R.E. Upshur, "Your liberty or your life: Reciprocity in the use of restrictive measures in contexts of contagion," Journal of Bioethical Inquiry, Vol. 6, No. 2, pp. 207-217, 2009. https://doi.org/10.1007/s11673-009-9149-2
  31. M. Ryan, "In defence of digital contact-tracing: human rights, South Korea and Covid-19," International Journal of Pervasive Computing and Communications, 2020. http://doi.org/10.1108/IJPCC-07-2020-0081
  32. D.J. Hunter, "Covid-19 and the stiff upper lip-the pandemic response in the United Kingdom," New England Journal of Medicine, Vol. 382, No. 16, p. e31, 2020. https://doi.org/10.1056/NEJMp2005755
  33. M. Maor and M. Howlett, "Explaining variations in state COVID-19 responses: psychological, institutional, and strategic factors in governance and public policy-making," Policy Design and Practice, Vol. 3, No. 3, pp. 228-241, 2020. https://doi.org/10.1080/25741292.2020.1824379
  34. N. Ferguson et al., "Report 9: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID19 mortality and healthcare demand," Imperial College London, Vol. 10, No. 77482, pp. 491-497, 2020. https://doi.org/10.25561/77482 
  35. Vox. https://www.vox.com/ (accessed September, 2023).
  36. E. Clark, E.Y. Chiao, and E.S. Amirian, "Why contact tracing efforts have failed to curb coronavirus disease 2019 (covid-19) transmission in much of the united states," Clin. Infect. Dis., Vol. 72, No. 9, pp. e415-e419, 2021. https://doi.org/10.1093/cid/ciaa1155
  37. H.D. Xu and R. Basu, "How the United States Flunked the COVID-19 test: some observations and several lessons," The American Review of Public Administration, Vol. 50, No. 6-7, pp. 568-576, 2020. https://doi.org/10.1177/0275074020941701
  38. L. Matrajt and T. Leung, "Evaluating the effectiveness of social distancing interventions to delay or flatten the epidemic curve of coronavirus disease," Emerging infectious diseases, Vol. 26, No. 8, p. 1740, 2020. https://doi.org/10.3201/eid2608.201093
  39. A.I. Friedson, D. McNichols, J.J. Sabia, and D. Dave, "Did California's shelter-in-place order work? Early coronavirus-related public health effects," National Bureau of Economic Research, 2020. https://doi.org/10.3386/w26992
  40. H. Liu, C. Chen, R. Cruz-Cano, J.L. Guida, and M. Lee, "Public Compliance With Social Distancing Measures and SARS-CoV-2 Spread: A Quantitative Analysis of 5 States," Public Health Rep., Vol. 136, No. 4, pp. 475-482, 2021. https://doi.org/10.1177/00333549211011254
  41. D. Graber, "Political Languages," in Handbook of Political Communication, D. Nimmo and K. Sanders Eds. Beverly Hills: Sage, 1981, pp. 192-224.
  42. P.A. Chilton, Analysing political discourse: Theory and practice. Psychology Press, 2004. https://doi.org/10.4324/9780203561218
  43. A. Nikolopoulou and E. Psyllakou, "The COVID-19 lockdown in Greece. Politicians, experts and public awareness campaigns in search of legitimisation," DiscourseNet Collaborative Working Paper Series, 2020.
  44. M.E. Belanger and S. Lavenex, "Communicating mobility restrictions during the Covid-19 crisis on Twitter: The legitimacy challenge," Swiss Polit. Sci. Rev., Vol. 27, No. 4, pp. 822-839, 2021. https://doi.org/10.1111/spsr.12494
  45. M. Reisigl, "11. Rhetoric of political speeches," Handbook of communication in the public sphere, Vol. 4, p. 243, 2008. https://doi.org/10.1515/9783110198980.3.243
  46. J. Etienne, "Compliance theory: A goal framing approach," Law & Pol'y, Vol. 33, No. 3, pp. 305-333, 2011. https://doi.org/10.1111/j.1467-9930.2011.00340.x
  47. A.E. Aiello et al., "Research findings from nonpharmaceutical intervention studies for pandemic influenza and current gaps in the research," Am. J. Infect. Control, Vol. 38, No. 4, pp. 251-258, 2010. https://doi.org/10.1016/j.ajic.2009.12.007
  48. K. Murphy, H. Williamson, E. Sargeant, and M. McCarthy, "Why people comply with COVID-19 social distancing restrictions: Self-interest or duty?," Australian & New Zealand Journal of Criminology, Vol. 53, No. 4, pp. 477-496, 2020. https://doi.org/10.1177/0004865820954484
  49. A. Al-Hasan, D. Yim, and J. Khuntia, "Citizens' adherence to COVID-19 mitigation recommendations by the government: a 3-country comparative evaluation using web-based cross-sectional survey data," J. Med. Internet Res., Vol. 22, No. 8, p. e20634, 2020. https://doi.org/10.2196/20634
  50. W. De Neys, M. Raoelison, E. Boissin, A. Voudouri, B. Bago, and M. Bialek, "Moral outrage and social distancing: bad or badly informed citizens?," 2020. https://doi.org/10.31234/osf.io/j9h76
  51. H. Xu et al., "Relationship between COVID-19 infection and risk perception, knowledge, attitude, and four nonpharmaceutical interventions during the late period of the COVID-19 epidemic in China: online cross-sectional survey of 8158 adults," J. Med. Internet Res., Vol. 22, No. 11, p. e21372, 2020. https://doi.org/10.2196/21372
  52. P.A. Glasow, "Fundamentals of survey research methodology," Retrieved January, Vol. 18, p. 2013, 2005.
  53. M.S. Deiner et al., "Facebook and Twitter vaccine sentiment in response to measles outbreaks," Health informatics journal, Vol. 25, No. 3, pp. 1116-1132, 2019. https://doi.org/10.1177/1460458217740723
  54. J. Van Dijck, "Users like you? Theorizing agency in user-generated content," Media, culture & society, Vol. 31, No. 1, pp. 41-58, 2009. https://doi.org/10.1177/0163443708098245
  55. G. Bouvier and D. Machin, "Critical discourse analysis and the challenges and opportunities of social media," Review of communication, Vol. 18, No. 3, pp. 178-192, 2018. https://doi.org/10.1080/15358593.2018.1479881
  56. T. Hale et al. A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker).
  57. T. Hale et al. "Codebook for the Oxford Covid-19 Government Response Tracker." https://github.com/OxCGRT/covid-policy-tracker/blob/master/documentation/codebook.md (accessed 12 September, 2023).
  58. N. Singh, D.P. Singh, and B. Pant, "A comprehensive study of big data machine learning approaches and challenges," in 2017 International Conference on Next Generation Computing and Information Systems (ICNGCIS), 2017: IEEE, pp. 80-85. https://doi.org/10.1109/ICNGCIS.2017.14
  59. Twitter Blog, "The 2014 #YearOnTwitter," https://url.kr/kt3h8g (accessed September, 2023).
  60. S. Taneja and M. Taneja, "Big Data And Twitter," International Journal Of Research In Computer Applications And Robotics. Vol, Vol. 2, pp. 144-150, 2014.
  61. B. Liu, Web data mining: exploring hyperlinks, contents, and usage data. Springer, 2011. https://doi.org/10.1007/978-3-642-19460-3
  62. H.T. Williams, J.R. McMurray, T. Kurz, and F.H. Lambert, "Network analysis reveals open forums and echo chambers in social media discussions of climate change," Global Environ. Change, Vol. 32, pp. 126-138, 2015. https://doi.org/10.1016/j.gloenvcha.2015.03.006
  63. S.-A.N. Alexandropoulos, S.B. Kotsiantis, and M.N. Vrahatis, "Data preprocessing in predictive data mining," The Knowledge Engineering Review, Vol. 34, 2019. http://doi.org/10.1017/S026988891800036X
  64. Y. Kim and S. Lee, "Combinations of Text Preprocessing and Word Embedding Suitable for Neural Network Models for Document Classification," Journal of KIISE, Vol. 45, No. 7, pp. 690-700, 2018. https://doi.org/10.5626/JOK.2018.45.7.690 
  65. J. Camacho-Collados and M.T. Pilehvar, "On the role of text preprocessing in neural network architectures: An evaluation study on text categorization and sentiment analysis," arXiv preprint arXiv:1707.01780, 2017. https://doi.org/10.48550/arXiv.1707.01780
  66. M. Taboada, "Sentiment analysis: An overview from linguistics," Annual Review of Linguistics, Vol. 2, pp. 325-347, 2016. https://doi.org/10.1146/annurev-linguistics-011415-040518
  67. B. Liu, Sentiment analysis: Mining opinions, sentiments, and emotions. Cambridge university press, 2020. https://doi.org/10.1017/9781108639286
  68. Y. Kim, "Convolutional Neural Networks for Sentence Classification," in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, 2014: Association for Computational Linguistics, in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1746-1751. https://doi.org/10.3115/v1/D14-1181
  69. Z. Jianqiang, G. Xiaolin, and Z. Xuejun, "Deep convolution neural networks for twitter sentiment analysis," IEEE Access, Vol. 6, pp. 23253-23260, 2018. https://doi.org/10.1109/ACCESS.2017.2776930
  70. TheJoongAng. https://www.joongang.co.kr/ (accessed September, 2023).
  71. BBC. https://bbc.com (accessed September, 2023).
  72. KCDC. http://ncov.mohw.go.kr/ (accessed September, 2023).
  73. GOV.UK. https://www.gov.uk/ (accessed September, 2023).
  74. M. Jit, T. Jombart, E.S. Nightingale, A. Endo, S. Abbott, and W.J. Edmunds, "Estimating number of cases and spread of coronavirus disease (COVID-19) using critical care admissions, United Kingdom, February to March 2020," Eurosurveillance, Vol. 25, No. 18, p. 2000632, 2020. https://doi.org/10.2807/1560-7917.es.2020.25.18.2000632
  75. H. Ritchie et al. "Coronavirus Pandemic (COVID-19)." https://ourworldindata.org/coronavirus (accessed 12 September, 2023).
  76. TheGuardian. https://www.theguardian.com/international (accessed September, 2023).
  77. EveningStandard. https://www.standard.co.uk (accessed September, 2023).
  78. M.L. Holshue et al., "First case of 2019 novel coronavirus in the United States," New England Journal of Medicine, 2020. 
  79. V.E. Pitzer et al., "The impact of changes in diagnostic testing practices on estimates of COVID-19 transmission in the United States," American Journal of Epidemiology, Vol. 190, No. 9, pp. 1908-1917, 2021. https://doi.org/10.1093/aje/kwab089
  80. Forbes. https://www.forbes.com (accessed September, 2023).
  81. J. Kates, J. Michaud, and J. Tolbert. "Sayt-At-Home Orders to Fight COVID-19 in the United States; The Risks of a scattershot approach." https://www.kff.org/policy-watch/stay-at-home-orders-to-fight-covid19/ (accessed 12 September, 2023).
  82. TheWashingtonPost. https://www.washingtonpost.com (accessed September, 2023).
  83. S. Decker, H. Peele, and M. Riser-Kositsky, "The coronavirus spring: The historic closing of U.S. schools (a timeline)," in EducationWeek, ed, 2020.
  84. T.G. Weiss, D.C. Seyle, and K. Coolidge, "The rise of non-state actors in global governance: Opportunities and limitations," 2013.
  85. "Travel restrictions issued by states in response to the coronavirus (COVID-19) pandemic, 2020-2021," in Ballotpedia, ed, 2021.
  86. J.G. Lu, P. Jin, and A.S. English, "Collectivism predicts mask use during COVID-19," Proceedings of the National Academy of Sciences, Vol. 118, No. 23, 2021. https://doi.org/10.1073/pnas.2021793118
  87. CBS. https://www.cbsnews.com (accessed September, 2023).
  88. M.H. Goldberg et al., "Mask-wearing increased after a government recommendation: A natural experiment in the US during the COVID-19 pandemic," Frontiers in Communication, Vol. 5, p. 44, 2020. https://doi.org/10.3389/fcomm.2020.00044
  89. CNBC. https://www.cnbc.com (accessed September, 2023).
  90. N. Harvey, "Behavioral fatigue: real phenomenon, naive construct, or policy contrivance?," Front. Psychol., Vol. 11, p. 2960, 2020. https://doi.org/10.3389/fpsyg.2020.589892 
  91. A.-L. Sibony, "The UK COVID-19 response: A behavioural irony?," European Journal of Risk Regulation, Vol. 11, No. 2, pp. 350-357, 2020. https://doi.org/10.1017/err.2020.22
  92. J.L. Benham et al., "Attitudes, current behaviours and barriers to public health measures that reduce COVID-19 transmission: A qualitative study to inform public health messaging," PLoS One, Vol. 16, No. 2, p. e0246941, 2021. https://doi.org/10.1371/journal.pone.0246941
  93. NPR. https://www.npr.org (accessed September, 2023).
  94. J. Wong, "Combating COVID-19 in Democratic Taiwan and South Korea," Current History, Vol. 119, No. 818, pp. 210-216, 2020. https://doi.org/10.1525/curh.2020.119.818.210
  95. Y. Lee and J.Y.Q. Li, "The role of communication transparency and organizational trust in publics' perceptions, attitudes and social distancing behaviour: A case study of the COVID-19 outbreak," J. Contingencies Crisis Manage., 2021. https://doi.org/10.1111/1468-5973.12354
  96. M.B. Petersen, A. Bor, F. Jorgensen, and M.F. Lindholt, "Transparent communication about negative features of COVID-19 vaccines decreases acceptance but increases trust," Proceedings of the National Academy of Sciences, Vol. 118, No. 29, 2021. https://doi.org/10.1073/pnas.2024597118
  97. H. Kim, "Lesson Learned from the Power of Open Data: Resolving the Mask Shortage Problem Caused by COVID-19 in South Korea," Sustainability, Vol. 13, No. 1, p. 278, 2021. https://doi.org/10.3390/su13010278
  98. TheKoreanEconomicDailly. http://newslabit.hankyung.com (accessed September, 2023).
  99. ForeignPolicy. https://foreignpolicy.com (accessed September, 2023).
  100. SomersetLive. https://www.somersetlive.co.uk/ (accessed September, 2023).