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.
View on arXiv