Estimating Time-Series Changes in Social Sensitivity for COVID-19 @ Twitter in Japan

Abstract

The global outbreak of COVID-19 is now putting enormous pressure on society to change our traditional social behavior. Government officials are also forced to make short-term decisions based on limited information for public health, and investor sentiment about the infection situation in each country has a significant impact on the stock market. In this paper, we attempt to visualize the time series of indexed social sentiment in Japan under the COVID-19 pandemic by using a neural network approach, and clarify changes in the sensitivity of citizens to the coronavirus. The sentiment was classified for Twitter tweets that matched the keywords for which the government was asked to restrict action, and sentiment trends were identified for the period from before the outbreak to the fifth wave of infection in Tokyo, Sapporo, Osaka, and Fukuoka. The indices obtained show a correlation with the number of infected cases by region and with national and local events, and in global cities such as Tokyo and Osaka as they experienced waves of infections and emergency declarations, sensitivity gradually became paralyzed, and parallel trends in sentiment waveforms were observed among regions.