Understand Research Hotspots Surrounding COVID-19 and Other Coronavirus Infections Using Topic Modeling

Mengying Dong; Xiaojun Cao; Mingbiao Liang; Lijuan Li; Huiying Liang; Guangjian Liu

Similar to the study of Md Mahbub Hossain MBBS (10), we did not identify any topic about social, economic, political, psychological, or cultural consequences of COVID-19 as well as other CoV infections. The WHO roadmap has noticed this and taken 'social sciences in the outbreak response' as a priority. The social science research community should act to assess the impact of a major public health crisis like COVID-19 on the complex network of modern societies (23, 24) . This present work has several limitations that must be acknowledged to apprehend the findings. First, as a computer model, topic modeling has difficulties with understanding nuances and subtext. We identified eight major topics with frequent appearances, whereas some specific topics such as virus natural history and human-animal interface were buried due to low proportions. Future work will attempt to develop an approach to detect and visualize the conceptual sub-domains. Second, it is also necessary to be aware that the granularity of terms used to label topics may vary a little for different topics. We have tried our best to keep them at the same level through carefully curating the top words in each topic, as well as reviewing text intention of the corresponding publications. Third, the LDA model only provides a cross-sectional profile of the coronavirus research, which is methodologically different from academic disciplines . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.