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.
Large-scale, Language-agnostic Discourse Classification of Tweets During COVID-19
Language-agnostic tweet representations enable efficient classification of large-scale public discourse about pandemics using computationally lightweight machine learning models.
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- 2020
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- arXiv 2020
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- 1
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- arxiv.org/abs/2008.00461v2ARXIV-DEFAULT
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