论文标题
共同19日大流行期间的公共话语和情感：在Twitter上使用潜在的Dirichlet分配进行主题建模
Public discourse and sentiment during the COVID-19 pandemic: using Latent Dirichlet Allocation for topic modeling on Twitter
论文作者
论文摘要
该研究旨在了解Twitter用户对Covid-19的话语和心理反应。 We use machine learning techniques to analyze about 1.9 million Tweets (written in English) related to coronavirus collected from January 23 to March 7, 2020. A total of salient 11 topics are identified and then categorized into ten themes, including "updates about confirmed cases," "COVID-19 related death," "cases outside China (worldwide)," "COVID-19 outbreak in South Korea," "early signs of the outbreak in New约克，“钻石公主克鲁斯”，“经济影响”，“预防措施”，“当局”和“供应链”。结果不会在Twitter上揭示与治疗和症状相关的信息作为普遍的主题。情感分析表明，对冠状病毒未知本质的恐惧在所有主题中均占主导地位。还讨论了研究的含义和局限性。
The study aims to understand Twitter users' discourse and psychological reactions to COVID-19. We use machine learning techniques to analyze about 1.9 million Tweets (written in English) related to coronavirus collected from January 23 to March 7, 2020. A total of salient 11 topics are identified and then categorized into ten themes, including "updates about confirmed cases," "COVID-19 related death," "cases outside China (worldwide)," "COVID-19 outbreak in South Korea," "early signs of the outbreak in New York," "Diamond Princess cruise," "economic impact," "Preventive measures," "authorities," and "supply chain." Results do not reveal treatments and symptoms related messages as prevalent topics on Twitter. Sentiment analysis shows that fear for the unknown nature of the coronavirus is dominant in all topics. Implications and limitations of the study are also discussed.
