Abstract
The outbreak of the COVID-19 led 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 how Twitter users in the Greater Region (GR) and related countries react differently over time through conducting a data-driven exploratory study of COVID-19 information using machine learning and representation learning methods. We find that tweet 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 22 January 2020 to 5 June 2020, figuring out the main differences between GR and related countries.
Highlights
The outbreak of COVID-19 led to an outbreak of information in major online social networks (OSNs), including Twitter, Facebook, Instagram, and YouTube [1]
We find that tweet volume and COVID-19 daily cases in the Greater Region (GR) and related countries are correlated, and tweet volume can help predict COVID-19 daily cases, but this strong correlation only exists during the early period of the pandemic
To explore the correlation between tweet volume and COVID-19 daily cases in GR and the related countries, we introduce basic reproductive rate R0 and effective reproductive rate R(t) in epidemiology to slice the periods of the pandemic, and a spatio-temporal analysis of the correlation between tweet volume and daily cases in each period is conducted by Pearson Correlations (PC)
Summary
The outbreak of COVID-19 led to an outbreak of information in major online social networks (OSNs), including Twitter, Facebook, Instagram, and YouTube [1]. We screen a novel Twitter dataset of 22 January 2020 to 5 June 2020 which contains data from users with locations labelled in the GR, and related countries including Luxembourg, France, Germany and Belgium, and the COVID-19 related tweets from Chen et al’s dataset [20] This dataset will be shared with the public to advance related research. This study sheds light on how the Twitter users in the GR and related countries react differently over time through an interdisciplinary approach It may, help to understand changes in public concerns on Twitter during the pandemic, and in particular, the distinctive characteristics of topics in the GR, a relational city with high mobility
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