The COVID-19 pandemic forced countries all over the world to take
unprecedented measures like nationwide lockdowns. To adequately understand the
emotional and social repercussions, a large-scale reconstruction of how people
perceived these unexpected events is necessary but currently missing. We
address this gap through social media by introducing MERCURIAL (Multi-layer
Co-occurrence Networks for Emotional Profiling), a framework which exploits
linguistic networks of words and hashtags to reconstruct social discourse
describing real-world events. We use MERCURIAL to analyse 101,767 tweets from
Italy, the first country to react to the COVID-19 threat with a nationwide
lockdown. The data were collected between 11th and 17th March, immediately
after the announcement of the Italian lockdown and the WHO declaring COVID-19 a
pandemic. Our analysis provides unique insights into the psychological burden
of this crisis, focussing on: (i) the Italian official campaign for
self-quarantine (#iorestoacasa}), (ii) national lockdown (#italylockdown), and
(iii) social denounce (#sciacalli). Our exploration unveils evidence for the
emergence of complex emotional profiles, where anger and fear (towards
political debates and socio-economic repercussions) coexisted with trust,
solidarity, and hope (related to the institutions and local communities). We
discuss our findings in relation to mental well-being issues and coping
mechanisms, like instigation to violence, grieving, and solidarity. We argue
that our framework represents an innovative thermometer of emotional status, a
powerful tool for policy makers to quickly gauge feelings in massive audiences
and devise appropriate responses based on cognitive data.

使用社交媒体分析，研究了意大利国家封锁措施所带来的心理压力，结果发现采用情绪分析架构 MERCURIAL 可以有效地了解大众感受并帮助政策制定者更好地应对突发事件。