论文标题
Covid-19期间Twitter活动的因果建模
Causal Modeling of Twitter Activity During COVID-19
论文作者
论文摘要
了解公众关注和情感的特征是在不良健康事件中进行适当危机管理的必要前提。在Covid-19等大流行期间，这更为至关重要，因为风险管理的主要责任不是集中在一个机构中，而是在整个社会中分布。尽管许多研究在COVID-19大流行期间使用Twitter数据在描述性或预测性环境中使用了Twitter数据，但尚未研究公众关注的因果建模。在这项研究中，我们提出了一种因果推论方法，以发现和量化大流行特征（例如感染和死亡人数）与Twitter活动以及公众情绪之间的因果关系。我们的结果表明，所提出的方法可以成功捕获流行病学领域知识并确定影响公众注意力和情感的变量。我们认为，我们的工作通过区分与引起公众关注的事件相关的事件来区分事件，从而有助于临时人工学领域。
Understanding the characteristics of public attention and sentiment is an essential prerequisite for appropriate crisis management during adverse health events. This is even more crucial during a pandemic such as COVID-19, as primary responsibility of risk management is not centralized to a single institution, but distributed across society. While numerous studies utilize Twitter data in descriptive or predictive context during COVID-19 pandemic, causal modeling of public attention has not been investigated. In this study, we propose a causal inference approach to discover and quantify causal relationships between pandemic characteristics (e.g. number of infections and deaths) and Twitter activity as well as public sentiment. Our results show that the proposed method can successfully capture the epidemiological domain knowledge and identify variables that affect public attention and sentiment. We believe our work contributes to the field of infodemiology by distinguishing events that correlate with public attention from events that cause public attention.
