Exploring Public Responses to Government’s COVID-19 Pandemic Policies

Brenna Drummond 1 * , Aysun Bozanta 1
More Detail
1 Ryerson University, CANADA
* Corresponding Author
Online Journal of Communication and Media Technologies, Volume 12, Issue 2, Article No: e202212. https://doi.org/10.30935/ojcmt/11829
OPEN ACCESS   1411 Views   836 Downloads   Published online: 03 Mar 2022
Download Full Text (PDF)

ABSTRACT

The ongoing pandemic of coronavirus disease 2019 (COVID-19) has challenged governments worldwide and approaches to combating the spread and maintaining livelihoods have ranged significantly. The purpose of the study is to a) investigate whether social media, specifically Twitter, can be used to identify topics of discussion regarding governments’ COVID-19 policies, and; b) whether those discussions can be interpreted in a way that can support governments in making policy decisions. Real-time public responses to these policies are a matter of interest, as understanding the content of discussions and the attitudes expressed towards government approaches can support the development of more grounded solutions for large-scale policy issues. Latent Dirichlet Allocation (LDA) is used to identify topics of discussion alongside Valence Aware Dictionary for sEntiment Reasoning (VADER) sentiment analyzer. Text data from two different jurisdictions are used and examined side by side. The Oxford COVID-19 Government Response Tracker (OxCGRT) is used to standardize policy discussions. The results of the study found that: a) Individuals tweeted most frequently and most passionately about case and death rates in their jurisdictions. Particularly about the rates with respect to vulnerable populations, such as those in long term care, nursing homes, and health workers, and b) Tweets expressed frustrations with the communication, length of implementation, or lack of rationale behind policies, suggesting the way the policy is communicated and delivered impacts individuals’ sentiments.
Keywords: topic modelling, sentiment analysis, Twitter, government policy, public response, COVID-19, OxCGRT

CITATION

Drummond, B., & Bozanta, A. (2022). Exploring Public Responses to Government’s COVID-19 Pandemic Policies. Online Journal of Communication and Media Technologies, 12(2), e202212. https://doi.org/10.30935/ojcmt/11829

REFERENCES

  • Albalawi, Y., Carroll, N., & Nikolov, N. S. (2017). Opportunities for social media to support health communication in health crises. In The 3rd European Technology Assessment Conference.
  • Arpaci, I., Alshehabi, S., Al-Emran, M., Khasawneh, M., Mahariq, I., Abdeljawad, T., & Hassanien, A. E. (2020). Analysis of Twitter data using evolutionary clustering during the COVID-19 pandemic. Computers, Materials & Continua, 65(1), 193-204. https://doi.org/10.32604/cmc.2020.011489
  • Chen, C. W., Lee, S., Dong, M. C., & Taniguchi, M. (2021). What factors drive the satisfaction of citizens with governments’ responses to COVID-19? International Journal of Infectious Diseases, 102, 327-331. https://doi.org/10.1016/j.ijid.2020.10.050
  • Dey, R. K., Sarddar, D., Sarkar, I., Bose, R., & Roy, S. (2020). A literature survey on sentiment analysis techniques involving social media and online platforms. International Journal of Scientific & Technology Research, 9(5), 166-173.
  • Hutto, C., & Gilbert, E. (2014). Vader: A parsimonious rule-based model for sentiment analysis of social media text. In Proceedings of the International AAAI Conference on Web and Social Media (pp. 216-225). https://ojs.aaai.org/index.php/ICWSM/article/view/14550
  • Jelodar, H., Wang, Y., Orji, R., & Huang, S. (2020). Deep sentiment classification and topic discovery on novel coronavirus or COVID-19 online discussions: NLP using LSTM recurrent neural network approach. IEEE Journal of Biomedical and Health Informatics, 24(10), 2733-2742. https://doi.org/10.1109/JBHI.2020.3001216
  • Kaila, R. P., & Prasad, A. V. K. (2020). Informational flow on Twitter–Corona virus outbreak–topic modelling approach. International Journal of Advanced Research in Engineering and Technology, 11(3), 128-134.
  • Kaminska, O., Cornelis, C., & Hoste, V. (2021). Fuzzy-rough nearest neighbour approaches for emotion detection in tweets. In International Joint Conference on Rough Sets (pp. 231-246). Springer, Cham. https://doi.org/10.1007/978-3-030-87334-9_20
  • Müller, M., Salathé, M., & Kummervold, P. E. (2020). COVID-Twitter-BERT: A natural language processing model to analyse COVID-19 content on Twitter. arXiv preprint arXiv:2005.07503. https://doi.org/10.48550/arXiv.2005.07503
  • Petherick, A., Goldszmidt, R., Andrade, E. B., Furst, R., Pott, A., & Wood, A. (2021). A worldwide assessment of COVID-19 pandemic-policy fatigue. SSRN 3774252. https://doi.org/10.2139/ssrn.3774252
  • Roy, M., Moreau, N., Rousseau, C., Mercier, A., Wilson, A., & Atlani-Duault, L. (2020). Ebola and localized blame on social media: Analysis of Twitter and Facebook conversations during the 2014-2015 Ebola epidemic. Culture, Medicine, and Psychiatry, 44(1), 56-79. https://doi.org/10.1007/s11013-019-09635-8
  • Sabat, I., Neuman-Böhme, S., Varghese, N. E., Barros, P. P., Brouwer, W., van Exel, J., Schreyögg, J., & Stargardt, T. (2020). United but divided: Policy responses and people’s perceptions in the EU during the COVID-19 outbreak. Health Policy, 124(9), 909-918. https://doi.org/10.1016/j.healthpol.2020.06.009
  • Sanders, A. C., White, R. C., Severson, L. S., Ma, R., McQueen, R., Paulo, H. C. A., Zhang, Y., Ercikson, J. S., & Bennett, K. P. (2021). Unmasking the conversation on masks: Natural language processing for topical sentiment analysis of COVID-19 Twitter discourse. AMIA Joint Summits on Translational Science Proceedings, 2021, 555-564. https://doi.org/10.1101/2020.08.28.20183863
  • Sarsam, S. M., Al-Samarraie, H., Alzahrani, A. I., Alnumay, W., & Smith, A. P. (2021). A lexicon-based approach to detecting suicide-related messages on Twitter. Biomedical Signal Processing and Control, 65, 102355. https://doi.org/10.1016/j.bspc.2020.102355
  • Satu, Md. S., Khan, Md. I., Mahmud, M., Uddin, S., Summers, M. A., Quinn, J. M. W., & Moni, M. A. (2021). TClustVID: A novel machine learning classification model to investigate topics and sentiment in COVID-19 tweets. Knowledge-Based Systems, 226, 107126. https://doi.org/10.1016/j.knosys.2021.107126
  • Sibley, C. G., Greaves, L. M., Satherley, N., Wilson, M. S., Overall, N. C., Lee, C. H. J., Milojev, P., Bulbulia, J., Osborne, D., Milfont, T.L., Houkamau, C. A., Duck, I. M., Vickers-Jones, R., & Barlow, F. K. (2020). Effects of the COVID-19 pandemic and nationwide lockdown on trust, attitudes toward government, and well-being. American Psychologist, 75(5), 618-630. https://doi.org/10.1037/amp0000662
  • Sievert, C., & Shirley, K. (2014). LDAvis: A method for visualizing and interpreting topics. In Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces (pp. 63-70). https://doi.org/10.3115/v1/W14-3110
  • Vashishtha, S., & Susan, S. (2021). Highlighting keyphrases using senti-scoring and fuzzy entropy for unsupervised sentiment analysis. Expert Systems with Applications, 169(2), 114323. https://doi.org/10.1016/j.eswa.2020.114323