Spatial and sentiment analysis of public opinion toward COVID-19 pandemic using twitter data: At the early stage of vaccination

Abstract

During the crisis of Coronavirus pandemic, social media, like Twitter, have been the platforms on which people have been able to share their opinions and obtain information. The present study provides a detailed spatial-temporal analysis of the Twitter online discourse (approximately 280 thousand tweets) in Ohio and Michigan at the early stage of vaccination rollout (January 2021, till March 2021). This work aims to explore how people were feeling about the pandemic, the most frequent topics people were talking about, and how the topics spatially were distributed. Moreover, state government responses and important news were gathered to analyze their impacts on public opinion based on the temporal analysis of the tweets. In this project, Natural Language Processing using the LDA method was employed to identify 11 topics and 8 sub-topics in the Twitter data. The temporal analysis of topics shows the sensitivity of the online discourse to the significant state news and the local government's reactions to the pandemic. Moreover, the spatial distribution of Coronavirus-related tweets and sentiments demonstrates concentrations in the more populated urban areas with a high rate of COVID-19 cases in Ohio and Michigan. The government's economic and financial policies taken during this time, the vaccination timeline phases specified by each state, and the pandemic-related information can contribute to public opinion and sentiment trends. The findings of this study can help explore public demands, and reactions, follow the impacts of the local authorities' policies at the county level and manage their future responses to such a pandemic.