IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v17y2020i18p6542-d410645.html
   My bibliography  Save this article

Factors Influencing Rumour Re-Spreading in a Public Health Crisis by the Middle-Aged and Elderly Populations

Author

Listed:
  • Zhonggen Sun

    (School of Public Administration, Hohai University, Nanjing 211100, Jiangsu, China)

  • Xin Cheng

    (School of Public Administration, Hohai University, Nanjing 211100, Jiangsu, China)

  • Ruilian Zhang

    (Sustainable Minerals Institute, University of Queensland, Brisbane 4072, Australia)

  • Bingqing Yang

    (School of Public Administration, Hohai University, Nanjing 211100, Jiangsu, China)

Abstract

Due to discrimination and media literacy, middle-aged and elderly individuals have been easily reduced to marginalized groups in the identification of rumours during a public health crisis and can easily spread rumours repeatedly, which has a negative impact on pandemic prevention and social psychology. To further clarify the factors influencing their behaviours, this study used a questionnaire to survey a sample of 556 individuals in China and used multiple linear regression and analysis of variance to explore influencing factors during the coronavirus disease 2019 (COVID-19) pandemic. We found that, first, in the COVID-19 pandemic, middle-aged and elderly adults’ willingness to re-spread rumours is positively related to their degree of believing rumours and to personal anxiety and is negatively related to their rumour-discrimination ability and to their perception of serious consequences to rumour spreading. Second, the degree of believing rumours plays an intermediary role in the willingness to re-spread rumours. It plays a partial mediating role in the path of anxiety’s influence on behaviour, suggesting that an anxious person will spread a rumour even if he or she does not have a strong belief in the rumour. Third, interpersonal communication has a greater credibility and a greater willingness to re-spread than does mass communication. This suggests the importance of increasing public knowledge expertise and of reducing public panic. This also has important implications for the future design of public health policies.

Suggested Citation

  • Zhonggen Sun & Xin Cheng & Ruilian Zhang & Bingqing Yang, 2020. "Factors Influencing Rumour Re-Spreading in a Public Health Crisis by the Middle-Aged and Elderly Populations," IJERPH, MDPI, vol. 17(18), pages 1-14, September.
  • Handle: RePEc:gam:jijerp:v:17:y:2020:i:18:p:6542-:d:410645
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/17/18/6542/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/17/18/6542/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhonggen Sun & Bingqing Yang & Ruilian Zhang & Xin Cheng, 2020. "Influencing Factors of Understanding COVID-19 Risks and Coping Behaviors among the Elderly Population," IJERPH, MDPI, vol. 17(16), pages 1-16, August.
    2. Zhu, Linhe & Guan, Gui, 2019. "Dynamical analysis of a rumor spreading model with self-discrimination and time delay in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 533(C).
    3. Hu, Yuhan & Pan, Qiuhui & Hou, Wenbing & He, Mingfeng, 2018. "Rumor spreading model with the different attitudes towards rumors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 331-344.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Adrian Kwek & Luke Peh & Josef Tan & Jin Xing Lee, 2023. "Distractions, analytical thinking and falling for fake news: A survey of psychological factors," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-12, December.
    2. Qian Ding & Xingyu Luo, 2022. "People with High Perceived Infectability Are More Likely to Spread Rumors in the Context of COVID-19: A Behavioral Immune System Perspective," IJERPH, MDPI, vol. 20(1), pages 1-10, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jianhong Chen & Hongcai Ma & Shan Yang, 2023. "SEIOR Rumor Propagation Model Considering Hesitating Mechanism and Different Rumor-Refuting Ways in Complex Networks," Mathematics, MDPI, vol. 11(2), pages 1-22, January.
    2. Lu, Peng, 2019. "Heterogeneity, judgment, and social trust of agents in rumor spreading," Applied Mathematics and Computation, Elsevier, vol. 350(C), pages 447-461.
    3. Nwaibeh, E.A. & Chikwendu, C.R., 2023. "A deterministic model of the spread of scam rumor and its numerical simulations," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 207(C), pages 111-129.
    4. Wang, Mengyao & Pan, Qiuhui & He, Mingfeng, 2020. "The effect of individual attitude on cooperation in social dilemma," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 555(C).
    5. Guilherme Ferraz de Arruda & Lucas G. S. Jeub & Angélica S. Mata & Francisco A. Rodrigues & Yamir Moreno, 2022. "From subcritical behavior to a correlation-induced transition in rumor models," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    6. Jain, Lokesh, 2022. "An entropy-based method to control COVID-19 rumors in online social networks using opinion leaders," Technology in Society, Elsevier, vol. 70(C).
    7. Lu, Peng & Deng, Liping & Liao, Hongbing, 2019. "Conditional effects of individual judgment heterogeneity in information dissemination," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 335-344.
    8. Javier Cifuentes-Faura & Ursula Faura-Martínez & Matilde Lafuente-Lechuga, 2022. "Mathematical Modeling and the Use of Network Models as Epidemiological Tools," Mathematics, MDPI, vol. 10(18), pages 1-14, September.
    9. Li, Ming & Zhang, Hong & Georgescu, Paul & Li, Tan, 2021. "The stochastic evolution of a rumor spreading model with two distinct spread inhibiting and attitude adjusting mechanisms in a homogeneous social network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 562(C).
    10. Yunjuan Luo & Yang Cheng & Mingxiao Sui, 2021. "The Moderating Effects of Perceived Severity on the Generational Gap in Preventive Behaviors during the COVID-19 Pandemic in the U.S," IJERPH, MDPI, vol. 18(4), pages 1-12, February.
    11. Taixiang Duan & Zhonggen Sun & Guoqing Shi, 2021. "Sustained Effects of Government Response on the COVID-19 Infection Rate in China: A Multiple Mediation Analysis," IJERPH, MDPI, vol. 18(23), pages 1-16, November.
    12. Wang, Chaoqian, 2020. "Dynamics of conflicting opinions considering rationality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).
    13. Cheng, Yingying & Huo, Liang'an & Zhao, Laijun, 2022. "Stability analysis and optimal control of rumor spreading model under media coverage considering time delay and pulse vaccination," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
    14. Yao Hongxing & Zou Yushi, 2019. "Research on Rumor Spreading Model with Time Delay and Control Effect," Journal of Systems Science and Information, De Gruyter, vol. 7(4), pages 373-389, August.
    15. Yin, Qian & Wang, Zhishuang & Xia, Chengyi & Dehmer, Matthias & Emmert-Streib, Frank & Jin, Zhen, 2020. "A novel epidemic model considering demographics and intercity commuting on complex dynamical networks," Applied Mathematics and Computation, Elsevier, vol. 386(C).
    16. Alberto Sardella & Vittorio Lenzo & George A. Bonanno & Giorgio Basile & Maria C. Quattropani, 2021. "Expressive Flexibility and Dispositional Optimism Contribute to the Elderly’s Resilience and Health-Related Quality of Life during the COVID-19 Pandemic," IJERPH, MDPI, vol. 18(4), pages 1-14, February.
    17. Zhu, He & Ma, Jing & Li, Shan, 2019. "Effects of online and offline interaction on rumor propagation in activity-driven networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 1124-1135.
    18. Guiyun Liu & Junqiang Li & Zhongwei Liang & Zhimin Peng, 2021. "Dynamical Behavior Analysis of a Time-Delay SIRS-L Model in Rechargeable Wireless Sensor Networks," Mathematics, MDPI, vol. 9(16), pages 1-21, August.
    19. Zhu, Linhe & Zheng, Wenxin & Shen, Shuling, 2023. "Dynamical analysis of a SI epidemic-like propagation model with non-smooth control," Chaos, Solitons & Fractals, Elsevier, vol. 169(C).
    20. Xu, Hao & Li, Tao & Liu, Xiongding & Liu, Wenjin & Dong, Jing, 2019. "Spreading dynamics of an online social rumor model with psychological factors on scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 234-246.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:17:y:2020:i:18:p:6542-:d:410645. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.