School of Public Policy and Management, Tsinghua University, Beijing, China.
Institute for Contemporary China Studies, Tsinghua University, Beijing, China.
J Med Internet Res. 2023 Jul 24;25:e45757. doi: 10.2196/45757.
Stay-at-home orders were one of the controversial interventions to curb the spread of COVID-19 in the United States. The stay-at-home orders, implemented in 51 states and territories between March 7 and June 30, 2020, impacted the lives of individuals and communities and accelerated the heavy usage of web-based social networking sites. Twitter sentiment analysis can provide valuable insight into public health emergency response measures and allow for better formulation and timing of future public health measures to be released in response to future public health emergencies.
This study evaluated how stay-at-home orders affect Twitter sentiment in the United States. Furthermore, this study aimed to understand the feedback on stay-at-home orders from groups with different circumstances and backgrounds. In addition, we particularly focused on vulnerable groups, including older people groups with underlying medical conditions, small and medium enterprises, and low-income groups.
We constructed a multiperiod difference-in-differences regression model based on the Twitter sentiment geographical index quantified from 7.4 billion geo-tagged tweets data to analyze the dynamics of sentiment feedback on stay-at-home orders across the United States. In addition, we used moderated effects analysis to assess differential feedback from vulnerable groups.
We combed through the implementation of stay-at-home orders, Twitter sentiment geographical index, and the number of confirmed cases and deaths in 51 US states and territories. We identified trend changes in public sentiment before and after the stay-at-home orders. Regression results showed that stay-at-home orders generated a positive response, contributing to a recovery in Twitter sentiment. However, vulnerable groups faced greater shocks and hardships during the COVID-19 pandemic. In addition, economic and demographic characteristics had a significant moderating effect.
This study showed a clear positive shift in public opinion about COVID-19, with this positive impact occurring primarily after stay-at-home orders. However, this positive sentiment is time-limited, with 14 days later allowing people to be more influenced by the status quo and trends, so feedback on the stay-at-home orders is no longer positively significant. In particular, negative sentiment is more likely to be generated in states with a large proportion of vulnerable groups, and the policy plays a limited role. The pandemic hit older people, those with underlying diseases, and small and medium enterprises directly but hurt states with cross-cutting economic situations and more complex demographics over time. Based on large-scale Twitter data, this sociological perspective allows us to monitor the evolution of public opinion more directly, assess the impact of social events on public opinion, and understand the heterogeneity in the face of pandemic shocks.
居家令是美国为遏制 COVID-19 传播而采取的有争议的干预措施之一。2020 年 3 月 7 日至 6 月 30 日，51 个州和地区实施了居家令，这影响了个人和社区的生活，并加速了基于网络的社交媒体的大量使用。推特情绪分析可以为公共卫生应急措施提供有价值的见解，并有助于更好地制定和安排未来的公共卫生措施，以应对未来的公共卫生紧急情况。
本研究评估了居家令如何影响美国的推特情绪。此外，本研究旨在了解不同情况和背景的群体对居家令的反馈。此外，我们特别关注弱势群体，包括有潜在医疗条件的老年人、中小企业和低收入群体。
我们构建了一个多期差分差异回归模型，该模型基于从 74 亿个地理标记推文数据中量化的推特情绪地理指数，以分析全美对居家令的情绪反馈的动态。此外，我们还使用了调节效应分析来评估弱势群体的差异化反馈。
我们梳理了 51 个美国州和地区的居家令实施情况、推特情绪地理指数以及确诊病例和死亡人数。我们确定了居家令前后公众情绪的趋势变化。回归结果表明，居家令产生了积极的反应，有助于推特情绪的恢复。然而，弱势群体在 COVID-19 大流行期间面临更大的冲击和困难。此外，经济和人口统计学特征具有显著的调节作用。
本研究表明，公众对 COVID-19 的看法明显好转，这种积极影响主要发生在居家令之后。然而，这种积极情绪是有限的，14 天后，人们更容易受到现状和趋势的影响，因此对居家令的反馈不再具有显著的积极意义。特别是在弱势群体比例较大的州，负面情绪更容易产生，政策的作用有限。大流行直接影响到老年人、有潜在疾病的人和中小企业，但随着时间的推移，对经济情况交叉和人口结构更复杂的州的影响更大。基于大规模的推特数据，这种社会学视角使我们能够更直接地监测公众舆论的演变，评估社会事件对公众舆论的影响，并了解面对疫情冲击的异质性。
