Significance For effective pandemic response, policymakers need tools that can assess policy impacts in near real-time. This requires policymakers to monitor changes in public well-being due to policy interventions. Particularly, containment measures affect people’s mental well-being, yet changes in public emotions and sentiments are challenging to assess. Our work provides a solution by using social media posts to compute salient concerns and daily public sentiment values as a proxy of mental well-being. We demonstrate how public sentiment and concerns are impacted by various containment policy sub-types. This approach provides key benefits of using a data-driven approach to identify public concerns and provides near real-time assessment of policy impacts by computing daily public sentiment based on postings on social media.

中文翻译：

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使用社交媒体数据确定疫情期间遏制政策对公众情绪的影响


意义 为了有效应对大流行病，政策制定者需要能够近乎实时评估政策影响的工具。这要求政策制定者监测政策干预导致的公众福祉的变化。特别是，遏制措施会影响人们的心理健康，但公众情绪和情绪的变化却难以评估。我们的工作提供了一种解决方案，通过使用社交媒体帖子来计算突出问题和日常公众情绪值作为心理健康的指标。我们展示了公众情绪和担忧如何受到各种遏制政策子类型的影响。这种方法提供了使用数据驱动方法来识别公众担忧的主要好处，并通过根据社交媒体上的帖子计算每日公众情绪来提供近乎实时的政策影响评估。