论文标题
了解Covid-19使用医疗NLP的新闻报道
Understanding COVID-19 News Coverage using Medical NLP
论文作者
论文摘要
作为全球大流行，Covid-19爆发受到了全球媒体的关注。在这项研究中，我们分析了CNN和Guardian的新闻出版物，这是世界上两个最有影响力的媒体组织中的两个。该数据集包含36,000多种文章，并使用来自Spark NLP的医疗保健库中的临床和生物医学自然语言处理（NLP）模型进行了分析，该模型可以比以前的医疗概念更深入地分析。该分析涵盖了关键实体和短语，观察到的偏见以及随着时间的流逝而随着时间的流逝而变化，通过将矿的医疗症状，程序，药物和指导与常见的人口统计和职业组相关联。另一个分析是提取有关药物和疫苗制造商的不良药物事件，当主要新闻媒体报道时，这些事件会影响疫苗的犹豫。
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.
