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      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

      research-article
      , PhD 1 , , PhD 2 , , MD 3 , , MPH, MD, PhD 2 , 3 ,
      (Reviewer), (Reviewer)
      Journal of Medical Internet Research
      JMIR Publications
      coronavirus, anxiety, pandemic, online, health information exchange, topic modeling

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          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.

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          Most cited references23

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          Finding scientific topics.

          A first step in identifying the content of a document is determining which topics that document addresses. We describe a generative model for documents, introduced by Blei, Ng, and Jordan [Blei, D. M., Ng, A. Y. & Jordan, M. I. (2003) J. Machine Learn. Res. 3, 993-1022], in which each document is generated by choosing a distribution over topics and then choosing each word in the document from a topic selected according to this distribution. We then present a Markov chain Monte Carlo algorithm for inference in this model. We use this algorithm to analyze abstracts from PNAS by using Bayesian model selection to establish the number of topics. We show that the extracted topics capture meaningful structure in the data, consistent with the class designations provided by the authors of the articles, and outline further applications of this analysis, including identifying "hot topics" by examining temporal dynamics and tagging abstracts to illustrate semantic content.
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            Using internet searches for influenza surveillance.

            The Internet is an important source of health information. Thus, the frequency of Internet searches may provide information regarding infectious disease activity. As an example, we examined the relationship between searches for influenza and actual influenza occurrence. Using search queries from the Yahoo! search engine ( http://search.yahoo.com ) from March 2004 through May 2008, we counted daily unique queries originating in the United States that contained influenza-related search terms. Counts were divided by the total number of searches, and the resulting daily fraction of searches was averaged over the week. We estimated linear models, using searches with 1-10-week lead times as explanatory variables to predict the percentage of cultures positive for influenza and deaths attributable to pneumonia and influenza in the United States. With use of the frequency of searches, our models predicted an increase in cultures positive for influenza 1-3 weeks in advance of when they occurred (P < .001), and similar models predicted an increase in mortality attributable to pneumonia and influenza up to 5 weeks in advance (P < .001). Search-term surveillance may provide an additional tool for disease surveillance.
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              A correlated topic model of Science

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                Author and article information

                Contributors
                Journal
                J Med Internet Res
                J. Med. Internet Res
                JMIR
                Journal of Medical Internet Research
                JMIR Publications (Toronto, Canada )
                1439-4456
                1438-8871
                June 2020
                2 June 2020
                2 June 2020
                : 22
                : 6
                : e19455
                Affiliations
                [1 ] The Institute for Social Data Science Pohang University of Science and Technology Pohang Republic of Korea
                [2 ] National Cancer Control Institute National Cancer Center Goyang Republic of Korea
                [3 ] Department of Family Medicine National Cancer Center Goyang Republic of Korea
                Author notes
                Corresponding Author: Yeol Kim drheat@ 123456ncc.re.kr
                Author information
                https://orcid.org/0000-0003-0443-2591
                https://orcid.org/0000-0002-0726-1671
                https://orcid.org/0000-0003-2749-5122
                https://orcid.org/0000-0003-1142-1559
                Article
                v22i6e19455
                10.2196/19455
                7268668
                32463367
                db990d0f-4ed6-421e-bc1a-f69b46c21355
                ©Wonkwang Jo, Jaeho Lee, Junli Park, Yeol Kim. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 02.06.2020.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.

                History
                : 18 April 2020
                : 11 May 2020
                : 26 May 2020
                : 27 May 2020
                Categories
                Original Paper
                Original Paper

                Medicine
                coronavirus,anxiety,pandemic,online,health information exchange,topic modeling
                Medicine
                coronavirus, anxiety, pandemic, online, health information exchange, topic modeling

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