Suppr超能文献

韩国新型冠状病毒(COVID-19)疫情早期的在线信息交流与焦虑传播:结构主题模型与网络分析

Online Information Exchange and Anxiety Spread in the Early Stage of the Novel Coronavirus (COVID-19) Outbreak in South Korea: Structural Topic Model and Network Analysis.

作者信息

Jo Wonkwang, Lee Jaeho, Park Junli, Kim Yeol

机构信息

The Institute for Social Data Science, Pohang University of Science and Technology, Pohang, Republic of Korea.

National Cancer Control Institute, National Cancer Center, Goyang, Republic of Korea.

出版信息

J Med Internet Res. 2020 Jun 2;22(6):e19455. doi: 10.2196/19455.

Abstract

BACKGROUND

In case of a population-wide infectious disease outbreak, such as the novel coronavirus disease (COVID-19), people's online activities could significantly affect public concerns and health behaviors due to difficulty in accessing credible information from reliable sources, which in turn causes people to seek necessary information on the web. Therefore, measuring and analyzing online health communication and public sentiment is essential for establishing effective and efficient disease control policies, especially in the early stage of an outbreak.

OBJECTIVE

This study aimed to investigate the trends of online health communication, analyze the focus of people's anxiety in the early stages of COVID-19, and evaluate the appropriateness of online information.

METHODS

We collected 13,148 questions and 29,040 answers related to COVID-19 from Naver, the most popular Korean web portal (January 20, 2020, to March 2, 2020). Three main methods were used in this study: (1) the structural topic model was used to examine the topics in the online questions; (2) word network analysis was conducted to analyze the focus of people's anxiety and worry in the questions; and (3) two medical doctors assessed the appropriateness of the answers to the questions, which were primarily related to people's anxiety.

RESULTS

A total of 50 topics and 6 cohesive topic communities were identified from the questions. Among them, topic community 4 (suspecting COVID-19 infection after developing a particular symptom) accounted for the largest portion of the questions. As the number of confirmed patients increased, the proportion of topics belonging to topic community 4 also increased. Additionally, the prolonged situation led to a slight increase in the proportion of topics related to job issues. People's anxieties and worries were closely related with physical symptoms and self-protection methods. Although relatively appropriate to suspect physical symptoms, a high proportion of answers related to self-protection methods were assessed as misinformation or advertisements.

CONCLUSIONS

Search activity for online information regarding the COVID-19 outbreak has been active. Many of the online questions were related to people's anxieties and worries. A considerable portion of corresponding answers had false information or were advertisements. The study results could contribute reference information to various countries that need to monitor public anxiety and provide appropriate information in the early stage of an infectious disease outbreak, including COVID-19. Our research also contributes to developing methods for measuring public opinion and sentiment in an epidemic situation based on natural language data on the internet.

摘要

背景

在新型冠状病毒病(COVID-19)等全人群感染性疾病爆发的情况下,由于难以从可靠来源获取可信信息,人们的在线活动可能会显著影响公众关注和健康行为,进而导致人们在网络上寻求必要信息。因此,测量和分析在线健康传播及公众情绪对于制定有效且高效的疾病控制政策至关重要,尤其是在疫情爆发的早期阶段。

目的

本研究旨在调查在线健康传播的趋势,分析COVID-19早期阶段人们焦虑的焦点,并评估在线信息的适宜性。

方法

我们从韩国最受欢迎的网络门户Naver收集了13148个与COVID-19相关的问题和29040个答案(2020年1月20日至2020年3月2日)。本研究使用了三种主要方法:(1)结构主题模型用于检查在线问题中的主题;(2)进行词网络分析以分析问题中人们焦虑和担忧的焦点;(3)两名医生评估了问题答案的适宜性,这些问题主要与人们的焦虑相关。

结果

从问题中总共识别出50个主题和6个连贯的主题社区。其中,主题社区4(出现特定症状后怀疑感染COVID-19)占问题的最大比例。随着确诊患者数量的增加,属于主题社区4的主题比例也增加。此外,情况的持续导致与工作问题相关的主题比例略有增加。人们的焦虑和担忧与身体症状和自我保护方法密切相关。尽管怀疑身体症状相对合理,但与自我保护方法相关的答案中,有很大一部分被评估为错误信息或广告。

结论

关于COVID-19疫情的在线信息搜索活动一直很活跃。许多在线问题与人们的焦虑和担忧有关。相当一部分相应答案存在虚假信息或为广告。研究结果可为包括COVID-19在内的需要在传染病爆发早期监测公众焦虑并提供适当信息的各国提供参考信息。我们的研究也有助于基于互联网上的自然语言数据开发在疫情情况下测量公众舆论和情绪的方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e237/7268668/824a38fb3a3b/jmir_v22i6e19455_fig1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验