Network-based Topic Interaction Map for Big Data Mining of COVID-19 Biomedical Literature

TL;DR

This paper introduces a network-based framework that visualizes and analyzes topic interactions in COVID-19 biomedical literature, aiding researchers in understanding complex relationships within large-scale research data.

Contribution

It proposes a novel analytical approach combining topic modeling, network visualization, and trajectory analysis to explore topic interactions in big biomedical data.

Findings

Effective visualization of topic relationships in COVID-19 literature

Identification of meaningful words characterizing each topic

Demonstrated utility on PubMed COVID-19 research data

Abstract

Since the emergence of the worldwide pandemic of COVID-19, relevant research has been published at a dazzling pace, which yields an abundant amount of big data in biomedical literature. Due to the high volum of relevant literature, it is practically impossible to follow up the research manually. Topic modeling is a well-known unsupervised learning that aims to reveal latent topics from text data. In this paper, we propose a novel analytical framework for estimating topic interactions and effective visualization to improve topics' relationships. We first estimate topic-word distributions using the biterm topic model and estimate the topics' interaction based on the word distribution using the latent space item response model. We mapped these latent topics onto networks to visualize relationships among the topics. Moreover, in the proposed approach, we developed a score that is helpful in selecting meaningful words that characterize the topic. We figure out how topics are related by looking at how their relationships change. We do this with a "trajectory plot" that is made with different levels of word richness. These findings provide a thoroughly mined and intuitive representation of relationships between topics related to a specific research area. The application of this proposed framework to the PubMed literature demonstrates utility of our approach in understanding of the topic composition related to COVID-19 studies in the stage of its emergence.