Containment measures have been applied throughout the world to halt the COVID-19 pandemic. In the United States, several forms of lockdown have been adopted in different parts of the country, leading to heterogeneous epidemiological, social, and economic effects. Here, we present a spatio-temporal analysis of a Twitter dataset comprising 1.3 million geo-localized Tweets about lockdown, from January to May 2020. Through sentiment analysis, we classified Tweets as expressing positive or negative emotions about lockdown, demonstrating a change in perception during the course of the pandemic modulated by socio-economic factors. A transfer entropy analysis of the time series of Tweets unveiled that the emotions in different parts of the country did not evolve independently. Rather, they were mediated by spatial interactions, which were also related to socio-ecomomic factors and, arguably, to political orientations. This study constitutes a first, necessary step toward isolating the mechanisms underlying the acceptance of public health interventions from highly resolved online datasets.

世界各地已采取遏制措施来阻止 COVID-19 大流行。在美国，全国不同地区采取了多种形式的封锁，导致了不同的流行病学、社会和经济影响。在这里，我们对 Twitter 数据集进行了时空分析，该数据集包含 2020 年 1 月至 5 月关于锁定的 130 万条地理本地化推文。通过情绪分析，我们将推文分类为表达关于锁定的积极或消极情绪，这表明了看法的变化在受社会经济因素调节的大流行过程中。对推文时间序列的转移熵分析表明，该国不同地区的情绪并不是独立演变的。相反，它们是通过空间相互作用来调节的，空间相互作用也与社会经济因素有关，并且可以说与政治取向有关。这项研究是从高度解析的在线数据集中分离出接受公共卫生干预措施的机制的第一步，也是必要的一步。