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

被引:2
|
作者
Drummond, Brenna [1 ]
Bozanta, Aysun [1 ]
机构
[1] Ryerson Univ, Toronto, ON, Canada
关键词
topic modelling; sentiment analysis; Twitter; government policy; public response; COVID-19; OxCGRT;
D O I
10.30935/ojcmt/11829
中图分类号
G2 [信息与知识传播];
学科分类号
05 ; 0503 ;
摘要
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.
引用
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页数:14
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