  The objective of this work is to explore popular discourse about the COVID-19
pandemic and policies implemented to manage it. Using Natural Language
Processing, Text Mining, and Network Analysis to analyze corpus of tweets that
relate to the COVID-19 pandemic, we identify common responses to the pandemic
and how these responses differ across time. Moreover, insights as to how
information and misinformation were transmitted via Twitter, starting at the
early stages of this pandemic, are presented. Finally, this work introduces a
dataset of tweets collected from all over the world, in multiple languages,
dating back to January 22nd, when the total cases of reported COVID-19 were
below 600 worldwide. The insights presented in this work could help inform
decision makers in the face of future pandemics, and the dataset introduced can
be used to acquire valuable knowledge to help mitigate the COVID-19 pandemic.
