# LDA2Net Digging under the surface of COVID-19 scientific literature topics via a network-based approach

## TL;DR

This paper introduces LDA2Net, a new method combining topic modeling and network analysis to explore and visualize topics in the growing body of scientific literature on COVID-19.

## Contribution

LDA2Net enhances topic modeling by using bigram frequencies to build networks, revealing hidden structures in large text datasets.

## Key findings

- LDA2Net improves the visualization and exploration of topics at multiple levels of detail.
- The network-based approach reveals deeper insights into the structure of topics in scientific literature.
- The method is effective in capturing and representing sub-themes within broader research areas.

## Abstract

During the COVID-19 pandemic, the scientific literature related to SARS-COV-2 has been growing dramatically. These literary items encompass a varied set of topics, ranging from vaccination to protective equipment efficacy as well as lockdown policy evaluations. As a result, the development of automatic methods that allow an in-depth exploration of this growing literature has become a relevant issue, both to identify the topical trends of COVID-related research and to zoom-in on its sub-themes. This work proposes a novel methodology, called LDA2Net, which combines topic modelling and network analysis, to investigate topics under their surface. More specifically, LDA2Net exploits the frequencies of consecutive words pairs (i.e. bigram) to build those network structures underlying the hidden topics extracted from large volumes of text by Latent Dirichlet Allocation (LDA). Results are promising and suggest that the topic model efficacy is magnified by the network-based representation. In particular, such enrichment is noticeable when it comes to displaying and exploring the topics at different levels of granularity.