论文标题
通过挖掘多语言Twitter数据集了解Covid-19政策的感知
Understanding the perception of COVID-19 policies by mining a multilanguage Twitter dataset
论文作者
论文摘要
这项工作的目的是探讨有关19009年大流行的流行论述和为管理它而实施的政策。使用自然语言处理，文本挖掘和网络分析来分析与COVID-19大流行有关的推文的语料库，我们确定了对大流行的共同反应以及这些反应在整个时间的不同之处。此外，从这个大流行的早期开始，关于如何通过Twitter传递信息和错误信息的见解。最后，这项工作介绍了从世界各地收集的多种语言收集的推文的数据集，其历史可追溯至1月22日，当时COVID-19的总案例在全球范围内低于600。这项工作中提出的见解可以帮助您在未来的大流行时为决策者提供信息，并且引入的数据集可用于获取宝贵的知识，以帮助减轻Covid-19-19的大流行。
The objective of this work is to explore popular discourse about the COVID-19 pandemic and policies implemented to manage it. Using Natural Language Processing, Text Mining, and Network Analysis to analyze corpus of tweets that relate to the COVID-19 pandemic, we identify common responses to the pandemic and how these responses differ across time. Moreover, insights as to how information and misinformation were transmitted via Twitter, starting at the early stages of this pandemic, are presented. Finally, this work introduces a dataset of tweets collected from all over the world, in multiple languages, dating back to January 22nd, when the total cases of reported COVID-19 were below 600 worldwide. The insights presented in this work could help inform decision makers in the face of future pandemics, and the dataset introduced can be used to acquire valuable knowledge to help mitigate the COVID-19 pandemic.
