Discovering associations in COVID-19 related research papers
This work provides insights for researchers analyzing scientific literature during pandemics, but it is incremental as it applies existing methods to new data.
The study applied association rule text mining and information cartography to analyze COVID-19 research paper abstracts, identifying key words and their relationships to understand historical research responses to epidemics.
A COVID-19 pandemic has already proven itself to be a global challenge. It proves how vulnerable humanity can be. It has also mobilized researchers from different sciences and different countries in the search for a way to fight this potentially fatal disease. In line with this, our study analyses the abstracts of papers related to COVID-19 and coronavirus-related-research using association rule text mining in order to find the most interestingness words, on the one hand, and relationships between them on the other. Then, a method, called information cartography, was applied for extracting structured knowledge from a huge amount of association rules. On the basis of these methods, the purpose of our study was to show how researchers have responded in similar epidemic/pandemic situations throughout history.