Understanding COVID-19 News Coverage using Medical NLP
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 5 of its 18 references that are in the map. First posted on arXiv 19 Mar 2022.
Builds on
5 of 18 references.
References · 5
- 2020Biomedical Named Entity Recognition at Scalecited by 30
- 2021Spark NLP: Natural Language Understanding at Scalecited by 16
- 2021Deeper Clinical Document Understanding Using Relation Extractioncited by 7
- 2020Improving Clinical Document Understanding on COVID-19 Research with Spark NLPcited by 4
- 2022Mining Adverse Drug Reactions from Unstructured Mediums at Scalecited by 3
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Citing papers per year: 2024: 2.
2 papers.