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. Here, we construct an expressed fear database using 16 million social media posts generated by 536 thousand users between January 1st, 2019 and August 31st, 2020 in China. We employ deep learning techniques to detect the fear emotion within each post and apply topic models to extract the central fear topics. Based on this database, we find that sleep disorders ("nightmare" and "insomnia") take up the largest share of fear-labeled posts in the pre-pandemic period (January 2019-December 2019), and significantly increase during the COVID-19. We identify health and work-related concerns are the two major sources of fear induced by the COVID-19. We also detect gender differences, with females generating more posts containing the daily-life fear sources during the COVID-19 period. This research adopts a data-driven approach to trace back public emotion, which can be used to complement traditional surveys to achieve real-time emotion monitoring to discern societal concerns and support policy decision-making.
翻译:COVID-19作为全球健康危机,引发了前所未有的恐惧情绪。COVID-19的爆发除了恐惧被感染之外,还极大地扰乱了人们的日常生活,从而间接地引发了对COVID-19感染的强烈心理反应。在这里,我们利用中国2019年1月1日至2020年8月31日的5.36 000个用户生成的1 600万个社交媒体站,建立了一个明确表达的恐惧数据库。我们使用了深层次的学习技术来检测每篇文章中的恐惧情绪,并应用主题模型来提取核心恐惧话题。根据这个数据库,我们发现睡眠失调(“夜幕”和“失眠”)在扩张前时期(2019年1月至2019年12月)占据了恐惧标记的岗位的最大比例,并在COVID-19期间大幅增加了。我们发现健康和工作方面的关切是COVID-19引起恐惧的两大根源。我们还发现性别差异,女性在COVID-19期间产生了包含日常生活恐惧源的更多职位。我们发现,通过这一研究,采用了一种数据驱动的方法,以追溯公众决策意识,从而实现真实的情感监测。