微软学术图表上的COVID-19文献的前八个月一瞥：主题，引用上下文和Understanding
A Glimpse of the First Eight Months of the COVID-19 Literature on Microsoft Academic Graph: Themes, Citation Contexts, and Uncertainties
摘要：
随着世界各地的科学家寻找致命大流行背后的压倒性未知因素的答案，有关COVID-19的文献呈指数级增长。以如此快速的速度跟上文学的主体，不仅对活跃的研究人员，而且对整个社会都构成了重大挑战。虽然已经公开提供了许多数据资源，但分析和综合过程对于有效浏览大量不确定性程度较高的信息至关重要，这仍然是一个重大瓶颈。我们介绍了一种通用方法，在处理研究领域（如COVID-19）在多个粒度级别上快速增长的情况时，该方法有助于数据汇集和意义构建过程。该方法集成了结构和时间模式的分析，在学术出版物的主题浓度和类型的不确定性，可能会提供额外的洞察未知的复杂性的划定。我们展示了该方法在COVID-19文献研究中的应用。
英文摘要：
As scientists worldwide search for answers to the overwhelmingly unknown behind the deadly pandemic, the literature concerning COVID-19 has been growing exponentially. Keeping abreast of the body of literature at such a rapidly advancing pace poses significant challenges not only to active researchers but also to the society as a whole. Although numerous data resources have been made openly available, the analytic and synthetic process that is essential in effectively navigating through the vast amount of information with heightened levels of uncertainty remains a significant bottleneck. We introduce a generic method that facilitates the data collection and sense-making process when dealing with a rapidly growing landscape of a research domain such as COVID-19 at multiple levels of granularity. The method integrates the analysis of structural and temporal patterns in scholarly publications with the delineation of thematic concentrations and the types of uncertainties that may offer additional insights into the complexity of the unknown. We demonstrate the application of the method in a study of the COVID-19 literature.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
