  Quantifying the characteristics of public attention is an essential
prerequisite for appropriate crisis management during severe events such as
pandemics. For this purpose, we propose language-agnostic tweet representations
to perform large-scale Twitter discourse classification with machine learning.
Our analysis on more than 26 million COVID-19 tweets shows that large-scale
surveillance of public discourse is feasible with computationally lightweight
classifiers by out-of-the-box utilization of these representations.
