Sijia Li

TL;DR: The results showed that negative emotions and sensitivity to social risks increased, while the scores of positive emotions and life satisfaction decreased, and people were concerned more about their health and family, while less about leisure and friends.

TL;DR: In this article, the authors used machine learning techniques to analyze about 1.9 million tweets related to COVID-19 collected from January 23 to March 7, 2020 and found that fear for the unknown nature of the coronavirus is dominant in all topics.

TL;DR: Wang et al. as discussed by the authors explored the change of public emotion responses during COVID-19 in China by analyzing the linguistic features of posts on social media, and found that negative emotion words such as fear, disappointment, guilt, missing, anger, panic, blessing, faith, love, praise, and surprise increased saliently in the outbreak period compared with the initial period.

TL;DR: Findings increase the understanding of the psychological impact and cost of residential lockdown during the epidemic as the more negative changes in residents' SWB in developed areas imply greater demand of psychological intervention under residential lockdown.

TL;DR: In this article, psychological status scores for 32 individuals bereaved due to the COVID-19 pandemic were obtained using the online ecological recognition (OER) approach, and a sentiment analysis and independent sample t-test were performed to examine the differences between these two groups.