Public Opinions on COVID-19 Vaccines-A Spatiotemporal Perspective on Races and Topics Using a Bayesian-Based Method.

The COVID-19 pandemic has been sweeping across the United States of America since early 2020. The whole world was waiting for vaccination to end this pandemic. Since the approval of the first vaccine by the U.S. CDC on 9 November 2020, nearly 67.5% of the US population have been fully vaccinated by 10 July 2022. While quite successful in controlling the spreading of COVID-19, there were voices against vaccines. Therefore, this research utilizes geo-tweets and Bayesian-based method to investigate public opinions towards vaccines based on (1) the spatiotemporal changes in public engagement and public sentiment; (2) how the public engagement and sentiment react to different vaccine-related topics; (3) how various races behave differently. We connected the phenomenon observed to real-time and historical events. We found that in general the public is positive towards COVID-19 vaccines. Public sentiment positivity went up as more people were vaccinated. Public sentiment on specific topics varied in different periods. African Americans' sentiment toward vaccines was relatively lower than other races.

Public demand estimation following disasters through Bayesian-based information integration

Public demand estimation is essential to effective relief resource distribution following disasters. However, previous studies are limited mainly due to the strong complexity, dynamicity, and nonlinearity of public demand. This research proposes an innovative data-driven approach to use the sample information (i.e., social media and surveys) to estimate public demand for the entire population. Twitter-based demand percentage (TDP) is applied as the predictor of actual demand percentage, while survey-based demand percentage (SDP) is taken as the ground truth of the actual demand percentage. The sampling bias of social media users is removed through a systematic process that contains the prediction of social media users’ races/ethnicities and the weighted aggregation of demand percentages. The sampling uncertainty of TDP and SDP is modeled with a Bayesian-based approach that integrates prior knowledge and observations from social media and surveys. The relationship between TDP and SDP is learned through a polynomial model, which is used to estimate future actual demand percentage solely using TDP. To illustrate the feasibility and applicability of the research, we studied public demand for COVID-19 vaccines in the US. Results demonstrate that TDP is a strong predictor of actual demand percentage. This research fully makes use of the advantages of sample information, i.e., the near-real-time nature of social media and the high reliability of surveys, to achieve a reliable and rapid estimation of public demand of the entire population.