Exploring the evolution of research topics during the COVID-19 pandemic

TL;DR

This paper introduces CORToViz, a visualization tool for analyzing the evolution of research topics in the extensive CORD-19 scientific corpus, utilizing advanced clustering, temporal mining, and interactive visualization techniques.

Contribution

The paper presents a novel method and visualization tool for exploring research topic dynamics in large scientific corpora, adaptable to various textual datasets.

Findings

Effective clustering of scientific articles by topics

Visualization of topic trends over time

Statistical testing for topic significance

Abstract

The COVID-19 pandemic has changed the research agendas of most scientific communities, resulting in an overwhelming production of research articles in a variety of domains, including medicine, virology, epidemiology, economy, psychology, and so on. Several open-access corpora and literature hubs were established; among them, the COVID-19 Open Research Dataset (CORD-19) has systematically gathered scientific contributions for 2.5 years, by collecting and indexing over one million articles. Here, we present the CORD-19 Topic Visualizer (CORToViz), a method and associated visualization tool for inspecting the CORD-19 textual corpus of scientific abstracts. Our method is based upon a careful selection of up-to-date technologies (including large language models), resulting in an architecture for clustering articles along orthogonal dimensions and extraction techniques for temporal topic mining. Topic inspection is supported by an interactive dashboard, providing fast, one-click visualization of topic contents as word clouds and topic trends as time series, equipped with easy-to-drive statistical testing for analyzing the significance of topic emergence along arbitrarily selected time windows. The processes of data preparation and results visualization are completely general and virtually applicable to any corpus of textual documents - thus suited for effective adaptation to other contexts.