Abstract:

The spread of COVID-19 has had a significant impact on the socio-economic area. It also resulted in an increase in the number of papers concerning effects of the pandemic in the economic indicators, including financial markets. They are in early stage of art. Many of them refer to psychological categories by introducing proxy variables for emotions, expectations, and attitudes. However, there is a gap in in-depth interpretation of these variables, which are built based on big social data including Twitter or Google Trends. The aim of this paper is to explore the COVID-19 hashtag data from March 2020 Twitter posts and considering some knowledge from psychology with emphasis on negative valance emotions including anger. Thus, we propose a big social data research scheme that includes getting to know the nature of emotions. We treat COVID-19 as a black swan for the stock market in which we enter the behavior of Twitter users around the selected hashtag. Presented exploratory data analysis suggests that anger comes during periods of increased Twitter activity. Thus, it can influence the decision-making process of an individual. For our research, this marks the beginning of future extended research in this area.