论文标题
在大区域Twitter上的COVID-19信息的探索性研究
An Exploratory Study of COVID-19 Information on Twitter in the Greater Region
论文作者
论文摘要
COVID-19的爆发导致主要在线社交网络（OSN）的大量信息。面对这种不断变化的情况，OSN已成为人们表达意见并寻求最新信息的重要平台。因此，关于OSN的讨论可能会成为现实的反映。本文旨在通过使用机器学习和代表性学习方法对Twitter COVID-19中的Twitter Covid-19信息进行数据驱动的探索性研究来弄清大区域（GR）的独特特征。我们发现，GR及相关国家 /地区的Tweet卷和Covid-19案例是相关的，但是这种相关性仅在大流行的特定时期存在。此外，我们绘制了从2020-01-22到2020-06-05的每个国家和地区的主题的变化，从而确定了GR与相关国家之间的主要区别。
The outbreak of the COVID-19 leads to a burst of information in major online social networks (OSNs). Facing this constantly changing situation, OSNs have become an essential platform for people expressing opinions and seeking up-to-the-minute information. Thus, discussions on OSNs may become a reflection of reality. This paper aims to figure out the distinctive characteristics of the Greater Region (GR) through conducting a data-driven exploratory study of Twitter COVID-19 information in the GR and related countries using machine learning and representation learning methods. We find that tweets volume and COVID-19 cases in GR and related countries are correlated, but this correlation only exists in a particular period of the pandemic. Moreover, we plot the changing of topics in each country and region from 2020-01-22 to 2020-06-05, figuring out the main differences between GR and related countries.
