Being a global pandemic, the COVID-19 outbreak received global media attention. In this study, we analyze news publications from CNN and The Guardian - two of the world's most influential media organizations. The dataset includes more than 36,000 articles, analyzed using the clinical and biomedical Natural Language Processing (NLP) models from the Spark NLP for Healthcare library, which enables a deeper analysis of medical concepts than previously achieved. The analysis covers key entities and phrases, observed biases, and change over time in news coverage by correlating mined medical symptoms, procedures, drugs, and guidance with commonly mentioned demographic and occupational groups. Another analysis is of extracted Adverse Drug Events about drug and vaccine manufacturers, which when reported by major news outlets has an impact on vaccine hesitancy.
@article{arxiv.2203.10338,
title = {Understanding COVID-19 News Coverage using Medical NLP},
author = {Ali Emre Varol and Veysel Kocaman and Hasham Ul Haq and David Talby},
journal= {arXiv preprint arXiv:2203.10338},
year = {2022}
}
Comments
Proceedings of the Text2Story'22 Workshop, Stavanger (Norway), 10-April-2022