An Exploratory Study of COVID-19 Information on Twitter in the Greater Region

TL;DR

This study analyzes Twitter data related to COVID-19 in the Greater Region and neighboring countries, revealing correlations between tweet volume and cases, and tracking topic changes over time to understand regional differences.

Contribution

It provides a data-driven exploration of COVID-19 discussions on Twitter, highlighting temporal correlations and regional topic variations using machine learning techniques.

Findings

Tweets volume correlates with COVID-19 cases during specific periods.

Distinct topic trends observed between the Greater Region and neighboring countries.

Temporal analysis reveals changing public concerns over time.

Abstract

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.