The COVID-19 pandemic disrupted everyone's life across the world. In this work, we characterize the subjective wellbeing patterns of 112 cities across the United States during the pandemic prior to vaccine availability, as exhibited in subreddits corresponding to the cities. We quantify subjective wellbeing using positive and negative affect. We then measure the pandemic's impact by comparing a community's observed wellbeing with its expected wellbeing, as forecasted by time series models derived from prior to the pandemic.We show that general community traits reflected in language can be predictive of community resilience. We predict how the pandemic would impact the wellbeing of each community based on linguistic and interaction features from normal times \textit{before} the pandemic. We find that communities with interaction characteristics corresponding to more closely connected users and higher engagement were less likely to be significantly impacted. Notably, we find that communities that talked more about social ties normally experienced in-person, such as friends, family, and affiliations, were actually more likely to be impacted. Additionally, we use the same features to also predict how quickly each community would recover after the initial onset of the pandemic. We similarly find that communities that talked more about family, affiliations, and identifying as part of a group had a slower recovery.

本文研究了美国112个城市在COVID-19疫情肆虐期间的主观幸福感，通过对城市相关的subreddits进行分析。我们采用了积极和消极的情感来量化主观幸福感，并使用时间序列模型预测疫情前的期望幸福感。研究发现，通过语言反映出的社区特征可以预测社区的弹性，那些更紧密联系和更高参与度的社区不太可能受到疫情的显著影响。而更多地谈论面对面社交，如朋友、家庭和归属感等社交关系的社区，则更容易受到影响。此外，我们使用相同的特征来预测每个社区在初期疫情暴发后的恢复速度，发现更强调家庭、社交和归属感的社区恢复速度更慢。

我们伴随彼此：量化社区主观幸福感和韧性