  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.
