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      Characterizing Weibo Social Media Posts From Wuhan, China During the Early Stages of the COVID-19 Pandemic: Qualitative Content Analysis

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          Abstract

          Background

          The COVID-19 pandemic has reached 40 million confirmed cases worldwide. Given its rapid progression, it is important to examine its origins to better understand how people’s knowledge, attitudes, and reactions have evolved over time. One method is to use data mining of social media conversations related to information exposure and self-reported user experiences.

          Objective

          This study aims to characterize the knowledge, attitudes, and behaviors of social media users located at the initial epicenter of the outbreak by analyzing data from the Sina Weibo platform in Chinese.

          Methods

          We used web scraping to collect public Weibo posts from December 31, 2019, to January 20, 2020, from users located in Wuhan City that contained COVID-19–related keywords. We then manually annotated all posts using an inductive content coding approach to identify specific information sources and key themes including news and knowledge about the outbreak, public sentiment, and public reaction to control and response measures.

          Results

          We identified 10,159 COVID-19 posts from 8703 unique Weibo users. Among our three parent classification areas, 67.22% (n=6829) included news and knowledge posts, 69.72% (n=7083) included public sentiment, and 47.87% (n=4863) included public reaction and self-reported behavior. Many of these themes were expressed concurrently in the same Weibo post. Subtopics for news and knowledge posts followed four distinct timelines and evidenced an escalation of the outbreak’s seriousness as more information became available. Public sentiment primarily focused on expressions of anxiety, though some expressions of anger and even positive sentiment were also detected. Public reaction included both protective and elevated health risk behavior.

          Conclusions

          Between the announcement of pneumonia and respiratory illness of unknown origin in late December 2019 and the discovery of human-to-human transmission on January 20, 2020, we observed a high volume of public anxiety and confusion about COVID-19, including different reactions to the news by users, negative sentiment after being exposed to information, and public reaction that translated to self-reported behavior. These findings provide early insight into changing knowledge, attitudes, and behaviors about COVID-19, and have the potential to inform future outbreak communication, response, and policy making in China and beyond.

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          Most cited references37

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          Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases From the Chinese Center for Disease Control and Prevention

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            The Impact of COVID-19 Epidemic Declaration on Psychological Consequences: A Study on Active Weibo Users

            COVID-19 (Corona Virus Disease 2019) has significantly resulted in a large number of psychological consequences. The aim of this study is to explore the impacts of COVID-19 on people’s mental health, to assist policy makers to develop actionable policies, and help clinical practitioners (e.g., social workers, psychiatrists, and psychologists) provide timely services to affected populations. We sample and analyze the Weibo posts from 17,865 active Weibo users using the approach of Online Ecological Recognition (OER) based on several machine-learning predictive models. We calculated word frequency, scores of emotional indicators (e.g., anxiety, depression, indignation, and Oxford happiness) and cognitive indicators (e.g., social risk judgment and life satisfaction) from the collected data. The sentiment analysis and the paired sample t-test were performed to examine the differences in the same group before and after the declaration of COVID-19 on 20 January, 2020. The results showed that negative emotions (e.g., anxiety, depression and indignation) and sensitivity to social risks increased, while the scores of positive emotions (e.g., Oxford happiness) and life satisfaction decreased. People were concerned more about their health and family, while less about leisure and friends. The results contribute to the knowledge gaps of short-term individual changes in psychological conditions after the outbreak. It may provide references for policy makers to plan and fight against COVID-19 effectively by improving stability of popular feelings and urgently prepare clinical practitioners to deliver corresponding therapy foundations for the risk groups and affected people.
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              Social Learning Theory and the Health Belief Model

              The Health Belief Model, social learning theory (recently relabelled social cognitive theory), self-efficacy, and locus of control have all been applied with varying success to problems of explaining, predicting, and influencing behavior. Yet, there is conceptual confusion among researchers and practitioners about the interrelationships of these theories and variables. This article attempts to show how these explanatory factors may be related, and in so doing, posits a revised explanatory model which incorporates self-efficacy into the Health Belief Model. Specifically, self-efficacy is proposed as a separate independent variable along with the traditional health belief variables of perceived susceptibility, severity, benefits, and barriers. Incentive to behave (health motivation) is also a component of the model. Locus of control is not included explicitly because it is believed to be incorporated within other elements of the model. It is predicted that the new formulation will more fully account for health-related behavior than did earlier formulations, and will suggest more effective behavioral interventions than have hitherto been available to health educators.
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                Author and article information

                Contributors
                Journal
                JMIR Public Health Surveill
                JMIR Public Health Surveill
                JPH
                JMIR Public Health and Surveillance
                JMIR Publications (Toronto, Canada )
                2369-2960
                Oct-Dec 2020
                7 December 2020
                7 December 2020
                : 6
                : 4
                : e24125
                Affiliations
                [1 ] Department of Healthcare Research and Policy University of California, San Diego - Extension La Jolla, CA United States
                [2 ] Global Health Policy and Data Institute San Diego, CA United States
                [3 ] S-3 Research LLC San Diego, CA United States
                [4 ] Masters Program in Computer Science Jacobs School of Engineering University of California, San Diego La Jolla, CA United States
                [5 ] Department of Anesthesiology School of Medicine University of California, San Diego La Jolla, CA United States
                [6 ] US Embassy National Cancer Institute National Institutes of Health Beijing China
                [7 ] Department of Anesthesiology and Division of Infectious Diseases and Global Public Health School of Medicine University of California, San Diego La Jolla, CA United States
                Author notes
                Corresponding Author: Tim Mackey tmackey@ 123456ucsd.edu
                Author information
                https://orcid.org/0000-0002-4507-1094
                https://orcid.org/0000-0002-4284-2092
                https://orcid.org/0000-0001-8670-6124
                https://orcid.org/0000-0002-8179-0619
                https://orcid.org/0000-0001-6816-5192
                https://orcid.org/0000-0001-6535-4940
                https://orcid.org/0000-0001-9801-4715
                https://orcid.org/0000-0002-2191-7833
                Article
                v6i4e24125
                10.2196/24125
                7722484
                33175693
                9bf722b9-ffdf-4c65-b15f-82b86652c198
                ©Qing Xu, Ziyi Shen, Neal Shah, Raphael Cuomo, Mingxiang Cai, Matthew Brown, Jiawei Li, Tim Mackey. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 07.12.2020.

                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 JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on http://publichealth.jmir.org, as well as this copyright and license information must be included.

                History
                : 4 September 2020
                : 9 October 2020
                : 29 October 2020
                : 6 November 2020
                Categories
                Original Paper
                Original Paper

                covid-19,infodemiology,infoveillance,infodemic,weibo,social media,content analysis,china,data mining,knowledge,attitude,behavior

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