Topic Modeling and Progression of American Digital News Media During the Onset of the COVID-19 Pandemic

TL;DR

This paper presents an NLP pipeline that analyzes and summarizes COVID-19 related digital news articles in the US, tracking how pandemic topics evolved over time to help readers understand the information landscape.

Contribution

It introduces a novel NLP framework combining unsupervised and semi-supervised learning, clustering, and topic modeling to analyze digital news media progression during COVID-19.

Findings

Identified key COVID-19 topics over time

Clustered articles based on similarity using community detection

Mapped the evolution of pandemic-related discussions

Abstract

Currently, the world is in the midst of a severe global pandemic, which has affected all aspects of people's lives. As a result, there is a deluge of COVID-related digital media articles published in the United States, due to the disparate effects of the pandemic. This large volume of information is difficult to consume by the audience in a reasonable amount of time. In this paper, we develop a Natural Language Processing (NLP) pipeline that is capable of automatically distilling various digital articles into manageable pieces of information, while also modelling the progression topics discussed over time in order to aid readers in rapidly gaining holistic perspectives on pressing issues (i.e., the COVID-19 pandemic) from a diverse array of sources. We achieve these goals by first collecting a large corpus of COVID-related articles during the onset of the pandemic. After, we apply unsupervised and semi-supervised learning procedures to summarize articles, then cluster them based on their similarities using the community detection methods. Next, we identify the topic of each cluster of articles using the BART algorithm. Finally, we provide a detailed digital media analysis based on the NLP-pipeline outputs and show how the conversation surrounding COVID-19 evolved over time.