Department of Operations Management, Center of Excellence in Operations and Information Management, Thammasat Business School, Thammasat University, Bangkok, Thailand.
Bangkok Christian Hospital, Bangkok, Thailand.
JMIR Public Health Surveill. 2020 Nov 11;6(4):e21978. doi: 10.2196/21978.
COVID-19 is a scientifically and medically novel disease that is not fully understood because it has yet to be consistently and deeply studied. Among the gaps in research on the COVID-19 outbreak, there is a lack of sufficient infoveillance data.
The aim of this study was to increase understanding of public awareness of COVID-19 pandemic trends and uncover meaningful themes of concern posted by Twitter users in the English language during the pandemic.
Data mining was conducted on Twitter to collect a total of 107,990 tweets related to COVID-19 between December 13 and March 9, 2020. The analyses included frequency of keywords, sentiment analysis, and topic modeling to identify and explore discussion topics over time. A natural language processing approach and the latent Dirichlet allocation algorithm were used to identify the most common tweet topics as well as to categorize clusters and identify themes based on the keyword analysis.
The results indicate three main aspects of public awareness and concern regarding the COVID-19 pandemic. First, the trend of the spread and symptoms of COVID-19 can be divided into three stages. Second, the results of the sentiment analysis showed that people have a negative outlook toward COVID-19. Third, based on topic modeling, the themes relating to COVID-19 and the outbreak were divided into three categories: the COVID-19 pandemic emergency, how to control COVID-19, and reports on COVID-19.
Sentiment analysis and topic modeling can produce useful information about the trends in the discussion of the COVID-19 pandemic on social media as well as alternative perspectives to investigate the COVID-19 crisis, which has created considerable public awareness. This study shows that Twitter is a good communication channel for understanding both public concern and public awareness about COVID-19. These findings can help health departments communicate information to alleviate specific public concerns about the disease.
COVID-19 是一种科学和医学上尚未完全了解的新型疾病，因为它尚未得到持续和深入的研究。在 COVID-19 爆发的研究空白中，缺乏足够的 infoveillance 数据。
本研究旨在提高对公众对 COVID-19 大流行趋势的认识，并揭示在大流行期间 Twitter 用户用英语发布的具有重要意义的关注主题。
对 Twitter 进行数据挖掘，以在 2020 年 12 月 13 日至 3 月 9 日期间共收集了 107990 条与 COVID-19 相关的推文。分析包括关键词的频率、情感分析和主题建模，以随时间识别和探索讨论主题。使用自然语言处理方法和潜在狄利克雷分配算法来识别最常见的推文主题，并根据关键词分析对聚类进行分类和识别主题。
结果表明，公众对 COVID-19 大流行有三个主要方面的认识和关注。首先，COVID-19 的传播和症状趋势可以分为三个阶段。其次，情感分析的结果表明，人们对 COVID-19 的前景持负面看法。第三，基于主题建模，与 COVID-19 和爆发相关的主题分为三类：COVID-19 大流行紧急情况、如何控制 COVID-19 以及 COVID-19 报告。
情感分析和主题建模可以从社交媒体上对 COVID-19 大流行讨论的趋势中产生有用的信息，以及从其他角度调查 COVID-19 危机，这引起了公众的极大关注。本研究表明，Twitter 是一个很好的沟通渠道，可以了解公众对 COVID-19 的关注和认识。这些发现可以帮助卫生部门传达信息，以减轻公众对疾病的特定关注。
