Rahman Md Mokhlesur, Ali G G Md Nawaz, Li Xue Jun, Samuel Jim, Paul Kamal Chandra, Chong Peter H J, Yakubov Michael
University of North Carolina at Charlotte, NC 28223, USA.
Khulna University of Engineering & Technology (KUET), Khulna 9203, Bangladesh.
Heliyon. 2021 Feb;7(2):e06200. doi: 10.1016/j.heliyon.2021.e06200. Epub 2021 Feb 6.
Investigating and classifying sentiments of social media users (e.g., positive, negative) towards an item, situation, and system are very popular among researchers. However, they rarely discuss the underlying socioeconomic factor associations for such sentiments. This study attempts to explore the factors associated with positive and negative sentiments of the people about reopening the economy, in the United States (US) amidst the COVID-19 global crisis. It takes into consideration the situational uncertainties (i.e., changes in work and travel patterns due to lockdown policies), economic downturn and associated trauma, and emotional factors such as depression. To understand the sentiment of the people about the reopening economy, Twitter data was collected, representing the 50 States of the US and Washington D.C, the capital city of the US. State-wide socioeconomic characteristics of the people (e.g., education, income, family size, and employment status), built environment data (e.g., population density), and the number of COVID-19 related cases were collected and integrated with Twitter data to perform the analysis. A binary logit model was used to identify the factors that influence people toward a positive or negative sentiment. The results from the logit model demonstrate that family households, people with low education levels, people in the labor force, low-income people, and people with higher house rent are more interested in reopening the economy. In contrast, households with a high number of family members and high income are less interested in reopening the economy. The accuracy of the model is reasonable (i.e., the model can correctly classify 56.18% of the sentiments). The Pearson chi-squared test indicates that this model has high goodness-of-fit. This study provides clear insights for public and corporate policymakers on potential areas to allocate resources, and directional guidance on potential policy options they can undertake to improve socioeconomic conditions, to mitigate the impact of pandemic in the current situation, and in the future as well.
调查和分类社交媒体用户对某一事物、情况和系统的情绪（如积极、消极）在研究人员中非常流行。然而，他们很少讨论这些情绪背后的社会经济因素关联。本研究试图探讨在美国新冠疫情全球危机期间，民众对重新开放经济持积极和消极情绪的相关因素。它考虑了情境不确定性（即由于封锁政策导致的工作和出行模式变化）、经济衰退及相关创伤，以及诸如抑郁等情绪因素。为了解民众对重新开放经济的情绪，收集了代表美国50个州和首都华盛顿特区的推特数据。收集了民众的全州社会经济特征（如教育程度、收入、家庭规模和就业状况）、建成环境数据（如人口密度）以及新冠相关病例数，并将其与推特数据整合以进行分析。使用二元逻辑回归模型来识别影响人们产生积极或消极情绪的因素。逻辑回归模型的结果表明，家庭户、低教育水平人群、劳动力、低收入人群以及房租较高的人群对重新开放经济更感兴趣。相比之下，家庭成员数量多和高收入的家庭对重新开放经济的兴趣较低。该模型的准确率较为合理（即该模型能正确分类56.18%的情绪）。皮尔逊卡方检验表明该模型具有很高的拟合优度。本研究为公共和企业政策制定者在资源分配的潜在领域提供了清晰的见解，并为他们为改善社会经济状况、减轻当前及未来疫情影响可采取的潜在政策选项提供了方向性指导。
