Tracking Short-Term Temporal Linguistic Dynamics to Characterize Candidate Therapeutics for COVID-19 in the CORD-19 Corpus

TL;DR

This paper explores temporal linguistic analysis of COVID-19 literature to identify and measure emerging trends related to candidate therapeutics, aiming to develop early screening tools for new treatments.

Contribution

It introduces a method for analyzing temporal linguistic dynamics in scientific literature to track candidate therapeutics for COVID-19.

Findings

Detected temporal changes in literature related to therapeutics

Proposed a framework for early therapeutic screening

Demonstrated potential for identifying emerging drug trends

Abstract

Scientific literature tends to grow as a function of funding and interest in a given field. Mining such literature can reveal trends that may not be immediately apparent. The CORD-19 corpus represents a growing corpus of scientific literature associated with COVID-19. We examined the intersection of a set of candidate therapeutics identified in a drug-repurposing study with temporal instances of the CORD-19 corpus to determine if it was possible to find and measure changes associated with them over time. We propose that the techniques we used could form the basis of a tool to pre-screen new candidate therapeutics early in the research process.