论文标题
瞥见Covid-19文献的前八个月有关Microsoft学术图：主题，引文环境和不确定性
A Glimpse of the First Eight Months of the COVID-19 Literature on Microsoft Academic Graph: Themes, Citation Contexts, and Uncertainties
论文作者
论文摘要
当科学家在全球搜索致命大流行背后的绝大多数未知的答案时，有关Covid-19的文献一直在成倍增长。保持这种迅速发展的速度与文学体系保持一致，这不仅对活跃的研究人员，而且对整个社会构成了重大挑战。尽管已经公开提供了许多数据资源，但是在有效地通过大量不确定性水平的大量信息导航至关重要的分析和合成过程仍然是一个显着的瓶颈。我们介绍了一种通用方法，该方法在处理研究领域的快速增长的景观（例如Covid-19）（在多个粒度的粒度上）时，可以促进数据收集和感知过程。该方法将学术出版物中的结构和时间模式的分析与主题浓度的描述以及可能为未知复杂性提供更多见解的不确定性的类型。我们证明了该方法在COVID-19文献研究中的应用。
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.
