Large-scale, Language-agnostic Discourse Classification of Tweets During COVID-19
This work addresses crisis management during pandemics by enabling large-scale surveillance of public discourse, though it appears incremental as it applies existing methods to new data.
The authors tackled the problem of quantifying public attention during the COVID-19 pandemic by proposing language-agnostic tweet representations for large-scale Twitter discourse classification, achieving feasibility with computationally lightweight classifiers on over 26 million tweets.
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.