论文标题
COVID-19的推文中的用户问题：一项探索性研究
User Questions from Tweets on COVID-19: An Exploratory Study
论文作者
论文摘要
社交媒体平台（例如Twitter）为有关健康问题的用户（人或患者）提供了合适的途径，以相互讨论和共享信息。 2019年12月，中国首次报道了一些冠状病毒病例。此后不久，由于病毒在世界其他地区的迅速传播，世界卫生组织（WHO）宣布了紧急状态。在这项工作中，我们使用了Twitter的COVID-19讨论和基于主题建模的自然语言处理（NLP）方法的自动提取，以发现与Tweets的Covid-19相关的主要问题。此外，我们创建了一个指定的实体识别（NER）模型，以确定四个不同类别的主要实体：疾病，毒品，人和组织。我们的发现可以帮助政策制定者和医疗保健组织了解Covid-19上的人们的问题，并且可以用于适当地解决他们。
Social media platforms, such as Twitter, provide a suitable avenue for users (people or patients) concerned on health questions to discuss and share information with each other. In December 2019, a few coronavirus disease cases were first reported in China. Soon after, the World Health Organization (WHO) declared a state of emergency due to the rapid spread of the virus in other parts of the world. In this work, we used automated extraction of COVID-19 discussion from Twitter and a natural language processing (NLP) method based on topic modeling to discover the main questions related to COVID-19 from tweets. Moreover, we created a Named Entity Recognition (NER) model to identify the main entities of four different categories: disease, drug, person, and organization. Our findings can help policy makers and health care organizations to understand the issues of people on COVID-19 and it can be used to address them appropriately.
