Understanding COVID-19 News Coverage using Medical NLP
This research addresses how media coverage influences public perception and vaccine hesitancy during the COVID-19 pandemic, but it is incremental as it applies existing NLP tools to new data.
The study analyzed over 36,000 COVID-19 news articles from CNN and The Guardian using medical NLP models to examine coverage patterns, including biases and changes over time, by correlating medical concepts with demographic groups and assessing adverse drug events.
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.