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Public Opinion Polarization by Individual Revenue from the Social Preference Theory

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  • Tinggui Chen

    (School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou 310018, China
    School of Management and E-Business, Zhejiang Gongshang University, Hangzhou 310018, China)

  • Qianqian Li

    (School of Management and E-Business, Zhejiang Gongshang University, Hangzhou 310018, China)

  • Peihua Fu

    (School of Management and E-Business, Zhejiang Gongshang University, Hangzhou 310018, China)

  • Jianjun Yang

    (Department of Computer Science and Information Systems, University of North Georgia, Oakwood, GA 30566, USA)

  • Chonghuan Xu

    (School of Business Administration, Zhejiang Gongshang University, Hangzhou 310018, China)

  • Guodong Cong

    (School of Tourism and Urban-Rural Planning, Zhejiang Gongshang University, Hangzhou 310018, China)

  • Gongfa Li

    (Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China)

Abstract

Social conflicts occur frequently during the social transition period and the polarization of public opinion happens occasionally. By introducing the social preference theory, the target of this paper is to reveal the micro-interaction mechanism of public opinion polarization. Firstly, we divide the social preferences of Internet users (network nodes) into three categories: egoistic, altruistic, and fair preferences, and adopt the revenue function to define the benefits obtained by individuals with different preferences among their interaction process so as to analyze their decision-making behaviors driven by the revenue. Secondly, the revenue function is used to judge the exit rules of nodes in a network, and then a dynamic network of spreading public opinion with the node (individual) exit mechanism is built based on a BA scale-free network. Subsequently, the influences of different social preferences, as well as individual revenue on the effect of public opinion polarization, are analyzed through simulation experiments. The simulation results show that (1) Different social preferences demonstrate different influences on the evolution of public opinions, (2) Individuals tend to interact with ones with different preferences, (3) The network with a single preference or a high aggregation is more likely to form public opinion polarization. Finally, the practicability and effectiveness of the proposed model are verified by a real case.

Suggested Citation

  • Tinggui Chen & Qianqian Li & Peihua Fu & Jianjun Yang & Chonghuan Xu & Guodong Cong & Gongfa Li, 2020. "Public Opinion Polarization by Individual Revenue from the Social Preference Theory," IJERPH, MDPI, vol. 17(3), pages 1-29, February.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:3:p:946-:d:316074
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    References listed on IDEAS

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    1. Tinggui Chen & Shiwen Wu & Jianjun Yang & Guodong Cong, 2019. "Risk Propagation Model and Its Simulation of Emergency Logistics Network Based on Material Reliability," IJERPH, MDPI, vol. 16(23), pages 1-18, November.
    2. Ernst Fehr & Klaus M. Schmidt, 1999. "A Theory of Fairness, Competition, and Cooperation," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 114(3), pages 817-868.
    3. Tinggui Chen & Qianqian Li & Jianjun Yang & Guodong Cong & Gongfa Li, 2019. "Modeling of the Public Opinion Polarization Process with the Considerations of Individual Heterogeneity and Dynamic Conformity," Mathematics, MDPI, vol. 7(10), pages 1-33, October.
    4. Daniel Navarro-Martinez & Graham Loomes & Andrea Isoni & David Butler & Larbi Alaoui, 2018. "Boundedly rational expected utility theory," Journal of Risk and Uncertainty, Springer, vol. 57(3), pages 199-223, December.
    5. Tinggui Chen & Shiwen Wu & Jianjun Yang & Guodong Cong & Gongfa Li, 2020. "Modeling of Emergency Supply Scheduling Problem Based on Reliability and Its Solution Algorithm under Variable Road Network after Sudden-Onset Disasters," Complexity, Hindawi, vol. 2020, pages 1-15, January.
    6. Kim, Jeeyeon & Kim, Mingyung & Choi, Jeonghye & Trivedi, Minakshi, 2019. "Offline social interactions and online shopping demand: Does the degree of social interactions matter?," Journal of Business Research, Elsevier, vol. 99(C), pages 373-381.
    7. María Guadalupe Medina Guevara & Héctor Vargas Rodríguez & Pedro Basilio Espinoza Padilla & José Luis Gozález Solís, 2018. "Evolution of Electoral Preferences for a Regime of Three Political Parties," Discrete Dynamics in Nature and Society, Hindawi, vol. 2018, pages 1-9, October.
    8. Levon Barseghyan & Francesca Molinari & Joshua C. Teitelbaum, 2016. "Inference under stability of risk preferences," Quantitative Economics, Econometric Society, vol. 7(2), pages 367-409, July.
    9. Jin Li & Renbin Xiao, 2017. "Agent-Based Modelling Approach for Multidimensional Opinion Polarization in Collective Behaviour," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 20(2), pages 1-4.
    10. Wayne-Roy Gayle & Natalia Khorunzhina, 2018. "Micro-Level Estimation of Optimal Consumption Choice With Intertemporal Nonseparability in Preferences and Measurement Errors," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(2), pages 227-238, April.
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    Cited by:

    1. Tinggui Chen & Yulong Wang & Jianjun Yang & Guodong Cong, 2021. "Modeling Multidimensional Public Opinion Polarization Process under the Context of Derived Topics," IJERPH, MDPI, vol. 18(2), pages 1-34, January.
    2. Peihua Fu & Bailu Jing & Tinggui Chen & Jianjun Yang & Guodong Cong, 2020. "Modeling Network Public Opinion Propagation with the Consideration of Individual Emotions," IJERPH, MDPI, vol. 17(18), pages 1-29, September.
    3. Ziyuan Liu & Zhi Li & Weiming Chen & Yunpu Zhao & Hanxun Yue & Zhenzhen Wu, 2020. "Path Optimization of Medical Waste Transport Routes in the Emergent Public Health Event of COVID-19: A Hybrid Optimization Algorithm Based on the Immune–Ant Colony Algorithm," IJERPH, MDPI, vol. 17(16), pages 1-18, August.

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