Content Aware Analysis of Scholarly Networks: A Case Study on CORD19 Dataset

TL;DR

This paper presents a novel method that integrates semantic topic information into scholarly network analysis using a hybrid HITS algorithm, applied to COVID-19 research articles from the CORD19 dataset, to improve understanding of research community structures.

Contribution

It introduces a new approach combining topic extraction with a modified HITS algorithm to analyze scholarly networks in the COVID-19 domain.

Findings

Topic-aware rankings differ from traditional citation-based rankings.

Incorporating semantic information enhances understanding of research influence.

The method reveals deeper insights into the COVID-19 scientific community.

Abstract

This paper investigates the relationships among key elements of the scientific research network, namely articles, researchers, and journals. We introduce a novel approach to use semantic information through the HITS algorithm-based propagation of topic information in the network. The topic information is derived by using the Named Entity Recognition and Entity Linkage. In our case, MedCAT is used to extract the topics from the CORD19 Dataset, which is a corpus of academic articles about COVID-19 and the coronavirus scientific network. Our approach focuses on the COVID-19 domain, utilizing the CORD-19 dataset to demonstrate the efficacy of integrating topic-related information within the citation framework. Through the application of a hybrid HITS algorithm, we show that incorporating topic data significantly influences article rankings, revealing deeper insights into the structure of the academic community.