Socioeconomic factors analysis for COVID-19 US reopening sentiment with Twitter and census data

Abstract

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.

Analysis of lockdown perception in the United States during the COVID-19 pandemic

“…Of the entire body of knowledge on the topic, only the study by Rahman et al [ 49 ] frames the sentiment analysis within a socio-economic perspective, although relying on a relatively small dataset. Other authors have used Twitter to study real-time events [ 50 ], mostly relying on a limited number of interactions [ 51 ] or tackling the analysis mainly from a theoretical point of view [ 52 ].…”

“…: V,-vol extract emotions conveyed by selected texts [34][35][36][37][38][39], in terms of a discrete classification or a continuous score. Twitter data on COVID-19 pandemic has been used to study reactions to the outbreak in different countries [40][41][42], benchmark and validate new models for natural language processing [43][44][45], perform sentiment analysis about the pandemic [46][47][48], and conduct analyses about a specific event [49].…”

Public Perceptions of COVID-19 Vaccines: Policy Implications from US Spatiotemporal Sentiment Analytics

“…The success experienced by countries across the world has depended on the effectiveness of their COVID-19 public policies pertaining to healthcare, communication, education, motivation and non-pharmaceutical interventions (NPIs), such as social distancing. Given that the COVID-19 vaccine was not available in the early stages of the outbreak, public policies initially focused on various NPIs (e.g., lockdown, restrictions on mass gathering, bans on travel, border closing, testing, and contact tracing), and economic stimuli (e.g., donations, loans, and debt relief) were implemented to contain the pandemic and mitigate the associated risks [14][15][16][17][18].…”

“…For example, Samuel et al [4] analyzed Twitter data to understand the state and evolution of the sentiment of fear that gripped people's state of mind while COVID-19 hit the US in February 2020. Similarly, just after the first wave of COVID-19 in the US, Samuel et al [5] and Rahman et al [18] aimed to gauge public sentiment towards reopening the US economy and the factors that control sentiments towards such moves, respectively.…”

“…Similarly, just after the first wave of COVID-19 in the US, Samuel et al. [ 5 ] and Rahman et al [ 18 ] aimed to gauge public sentiment towards reopening the US economy and the factors that control sentiments towards such moves, respectively.…”