A text-mining study on emotional cognition, understanding, and preventative behaviors during the COVID-19 pandemic

Abstract

Abstract Background This study aimed to look at emotions perceived about the attributes, prevention, diagnosis, and treatment of infectious diseases related to coronavirus disease (COVID-19) that were widespread across the world and identify their relevance to knowledge about infectious diseases and preventative behaviors. Methods Texts to measure emotional cognition were selected through a pre-test, and 282 people were chosen as participants based on the survey conducted for 20 days from August 19 to August 29, 2020, created with Google Forms. IBM SPSS Statistics 25.0 was used for the primary analysis, and the SNA package in R (version 4.0.2) was utilized to conduct the network analysis. Results It was found that universal negative emotions such as feeling “anxious” (65.5%), “afraid” (46.1%), and “scared” (32.7%) commonly appeared among most people. Also, they were found to be feeling both positive (“caring” [42.3%] and “strict” [28.2%]) and negative (“frustrating” [39.1%] and “isolated” [31.0%]) emotions about efforts to prevent and curb the spread of COVID-19. In terms of emotional cognition for the diagnosis and treatment of such diseases, “reliable” (43.3%) took the biggest ratio among the replies. The level of understanding about infectious diseases showed differences in emotional cognition, thereby affecting people’s emotions. However, no differences were found in the practice of preventative behaviors. Conclusions Emotions associated with cognition in the context of pandemic infectious diseases have been found to be mixed. Furthermore, it can be seen that feelings vary depending on the degree of understanding of the infectious disease.

Keywords

• Covid 19
• Emotional cognition
• Preventative behaviours
• Text-mining