Understanding COVID-19 News Coverage using Medical NLP

Autor:	Varol, Ali Emre, Kocaman, Veysel, Haq, Hasham Ul, Talby, David
Rok vydání:	2022
Předmět:	Computer Science - Computation and Language Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	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. Comment: Proceedings of the Text2Story'22 Workshop, Stavanger (Norway), 10-April-2022
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2203.10338 Zobrazit plný text záznamu View this record from Arxiv