The outbreak of the Coronavirus disease ( COVID-19 ) leads to an outbreak of pandemic information in major online social networks (OSNs). In the constantly changing situation, OSNs are becoming a critical conduit for people in expressing opinions and seek up-to-the-minute information. Thus, social behaviour on OSNs may become a predictor or reflection of reality. This paper aims to study the social behaviour of the public in the Greater Region (GR)  and related countries based on Twitter information with machine learning and representation learning methods. We find that tweets volume only can be a predictor of outbreaks in a particular period of the pandemic. Moreover, we map out the evolution of public behaviour in each country from 2020/01/22 to 2020/06/05 , figuring out the main differences in public behaviour between GR and related countries . Finally, we conclude that tweets volume of anti-contiguous measures may affect the effeteness of the government policy .
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 .
D
Coronavirus disease (
R
) leads to an outbreak of pandemic
leads to a burst of
R
In the
Facing this
R
are becoming a critical conduit for people in
have become an essential platform for people
R
seek
seeking
R
social behaviour
discussions
D
predictor or
R
study the social behaviour of the public in the
figure out the distinctive characteristics of the
A
through conducting a data-driven exploratory study of Twitter COVID-19 information in the GR
R
based on Twitter information with
using
R
only can be a predictor of outbreaks
and COVID-19 cases in GR and related countries are correlated, but this correlation only exists
R
map out the evolution of public behaviour
plot the changing of topics
R
from 2020/01/22 to 2020/06/05
and region from 2020-01-22 to 2020-06-05
D
in public behaviour
D
. Finally, we conclude that tweets volume of anti-contiguous measures may affect the effeteness of the government policy