Large-Scale, Language-Agnostic Discourse Classification of Tweets During COVID-19
Large-Scale, Language-Agnostic Discourse Classification of Tweets During COVID-19
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DOI：
10.3390/make2040032
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发表时间：
2020-12-01
影响因子：
3.9
通讯作者：
Gencoglu, Oguzhan
中科院分区：
文献类型：
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作者：
Gencoglu, Oguzhan
量化公众关注的特征是在流行病等严重事件期间进行适当危机管理的重要先决条件。为此，我们提出了与语言无关的推文表示，以通过机器学习执行大规模 Twitter 话语分类。我们对超过 2600 万条 2019 年冠状病毒病 (COVID-19) 推文的分析表明，通过开箱即用地利用这些表示形式，通过计算轻量级分类器对公共话语进行大规模监控是可行的。
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 coronavirus disease 2019 (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.