The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the
severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been
continuously affecting human lives and communities around the world in many
ways, from cities under lockdown to new social experiences. Although in most
cases COVID-19 results in mild illness, it has drawn global attention due to
the extremely contagious nature of SARS-CoV-2. Governments and healthcare
professionals, along with people and society as a whole, have taken any
measures to break the chain of transition and flatten the epidemic curve. In
this study, we used multiple data sources, i.e., PubMed and ArXiv, and built
several machine learning models to characterize the landscape of current
COVID-19 research by identifying the latent topics and analyzing the temporal
evolution of the extracted research themes, publications similarity, and
sentiments, within the time-frame of January- May 2020. Our findings confirm
the types of research available in PubMed and ArXiv differ significantly, with
the former exhibiting greater diversity in terms of COVID-19 related issues and
the latter focusing more on intelligent systems/tools to predict/diagnose
COVID-19. The special attention of the research community to the high-risk
groups and people with complications was also confirmed.

本研究通过多种数据源建立了多个机器学习模型对当前 COVID-19 研究场景进行了表征，包括识别潜在主题、分析出版物相似性和情感。结果表明 PubMed 和 ArXiv 中的研究类型存在显著区别，前者在 COVID-19 相关问题的多样性方面具有更大的多样性，后者则更关注预测 / 诊断 COVID-19 的智能系统 / 工具。研究团队对高危人群和并发症患者的特别关注也得到了证实。