论文标题
COVID-19期间的大规模，语言不足的话语分类
Large-scale, Language-agnostic Discourse Classification of Tweets During COVID-19
论文作者
论文摘要
量化公众关注的特征是在大流行等严重事件中进行适当危机管理的重要先决条件。为此，我们提出语言不可思议的推文表示，以通过机器学习执行大规模的Twitter话语分类。我们对超过2600万个COVID-19的推文的分析表明，通过对这些表示形式开箱即用的利用，对公共话语进行大规模监视是可行的。
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.
