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      Official social media and its impact on public behavior during the first wave of COVID-19 in China

      research-article
      BMC Public Health
      BioMed Central
      Information quality, Risk perception, Behavior intention, Continued use, COVID-19

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          Abstract

          Background

          During the COVID-19 pandemic, official social media became a critical channel for the public to obtain pandemic information. No matter the positive function or negative effect of information dissemination, it involves the public’s risk perception and behavior. This study was designed to contribute to the existing research on how official social media information quality (IQ) and risk perception (RP) affect preventive behavior (PB) and continued use behavior (CB) of official social media during the first wave of COVID-19.

          Methods

          The required data were extracted from a national online survey of the Chinese Mainland during March 24–30 2020, a random sample was asked to participate in the survey (n = 666). Data analysis was performed using regression analysis, structural equation modeling, mediating effect analysis, and one-way ANOVA analysis.

          Results

          The results show that IQ (10.010 ± 3.568) has direct and indirect positive impact on PB (9.475 ± 3.571), and has a low significant positive indirect impact on CB (3.739 ± 1.566). The IQ has a significant positive impact on RP (β = 0.548), which show that there is no “risk perception paradox” in COVID-19. Furth more, this study also provides new evidence indicating that RP mediates the relationship between IQ and PB. According to the region, gender, age and annual income, and there are significant differences in PB and CB.

          Conclusion

          The study findings have remarkable implications for improving the information quality and public behaviors. Too high or too low level of risk perception is not conducive to pandemic prevention and control. Official social media should indirectly affect information flow through the reasonable supply of pandemic information and constantly improve the quality of pandemic information to avoid public’s undue panic and excessive health concerns during this ongoing outbreak and subsequent national public emergency events.

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          Most cited references77

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          Correlation Coefficients

          Correlation in the broadest sense is a measure of an association between variables. In correlated data, the change in the magnitude of 1 variable is associated with a change in the magnitude of another variable, either in the same (positive correlation) or in the opposite (negative correlation) direction. Most often, the term correlation is used in the context of a linear relationship between 2 continuous variables and expressed as Pearson product-moment correlation. The Pearson correlation coefficient is typically used for jointly normally distributed data (data that follow a bivariate normal distribution). For nonnormally distributed continuous data, for ordinal data, or for data with relevant outliers, a Spearman rank correlation can be used as a measure of a monotonic association. Both correlation coefficients are scaled such that they range from -1 to +1, where 0 indicates that there is no linear or monotonic association, and the relationship gets stronger and ultimately approaches a straight line (Pearson correlation) or a constantly increasing or decreasing curve (Spearman correlation) as the coefficient approaches an absolute value of 1. Hypothesis tests and confidence intervals can be used to address the statistical significance of the results and to estimate the strength of the relationship in the population from which the data were sampled. The aim of this tutorial is to guide researchers and clinicians in the appropriate use and interpretation of correlation coefficients.
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            Structural Equations with Latent Variables

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              Is Open Access

              Risk perceptions of COVID-19 around the world

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                Author and article information

                Contributors
                liuhuan2017@xjtu.edu.cn
                Journal
                BMC Public Health
                BMC Public Health
                BMC Public Health
                BioMed Central (London )
                1471-2458
                3 March 2022
                3 March 2022
                2022
                : 22
                : 428
                Affiliations
                GRID grid.43169.39, ISNI 0000 0001 0599 1243, School of Journalism and New Media, , Xi’an Jiaotong University, ; Xi’an, China
                Article
                12803
                10.1186/s12889-022-12803-y
                8893355
                35241057
                b0e06784-9db0-4bec-ae25-cb969d1592b1
                © The Author(s) 2022

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 22 September 2021
                : 17 February 2022
                Categories
                Research
                Custom metadata
                © The Author(s) 2022

                Public health
                information quality,risk perception,behavior intention,continued use,covid-19
                Public health
                information quality, risk perception, behavior intention, continued use, covid-19

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