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

We Are in This Together: Quantifying Community Subjective Wellbeing and Resilience

In this paper we introduce a mathematical model that captures some of the
salient features of recommender systems that are based on popularity and that
try to exploit social ties among the users. We show that, under very general
conditions, the market always converges to a steady state, for which we are
able to give an explicit form. Thanks to this we can tell rather precisely how
much a market is altered by a recommendation system, and determine the power of
users to influence others. Our theoretical results are complemented by
experiments with real world social networks showing that social graphs prevent
large market distortions in spite of the presence of highly influential users.

通过一个数学模型研究了基于流行度和社交关系的推荐系统。结果表明，在一些普遍的条件下，市场总会收敛到一个稳定状态。通过实验进一步证明，实际社交网络中的社交图表现出一定的防止市场失真的作用，即使存在高度具有影响力的用户。