BACKGROUND
COVID-19, as a global health crisis, has triggered the fear emotion with unprecedented intensity. Besides the fear of getting infected, the outbreak of COVID-19 also created significant disruptions in people’s daily life and thus evoked intensive psychological responses indirect to COVID-19 infections.
OBJECTIVE
This study aims to develop novel digital trackers of public fear emotion during the COVID-19 pandemic, and to uncover meaningful topics that the citizens are concerned about to inform policy decision-making.
METHODS
We construct an expressed fear database using 16 million social media posts generated by 536 thousand users in China between January 1st, 2019 and August 31st, 2020. We employ Bidirectional Encoder Representations from Transformers (BERT) to detect the fear emotion within each post and apply BERTopic to extract the central fear topics.
RESULTS
We find that on average, 2.45% of posts per day having fear as the dominant emotion in 2019. This share spiked after the COVID-19 outbreak and peaked at 9.1% on the date that China’s epi-center Wuhan city announced lockdown. Among the fear posts, topics related to health takes the largest share (39%). Specifically, we find that posts regarding sleep disorders (Nightmare and Insomnia) have the most significant increase during the pandemic. We also observe gender heterogeneity in fear topics, with females being more concerned with health while males being more concerned with job.
CONCLUSIONS
Our work leverages the social media data coupled with computational methods to track the emotional response on a large scale and with high temporal granularity. While we conduct this research in a tracing back mode, it is possible to use such a method to achieve real-time emotion monitoring, thus serving as a helpful tool to discern societal concerns and aid for policy decision-making.