IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i2p472-d477026.html
   My bibliography  Save this article

Modeling Multidimensional Public Opinion Polarization Process under the Context of Derived Topics

Author

Listed:
  • 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)

  • Yulong Wang

    (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)

  • Guodong Cong

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

Abstract

With the development of Internet technology, the speed of information dissemination and accelerated updates result in frequent discussion of topics and expressions of public opinion. In general, multi-dimensional discussion topics related to the same event are often generated in the network, and the phenomenon of multi-dimensional public opinion polarization is formed under the mutual influence of groups. This paper targets the phenomenon of multi-dimensional public opinion polarization under topic-derived situations as the research object. Firstly, this paper identifies the factors influencing multi-dimensional public opinion polarization, including the mutual influence of different topic dimensions and the interaction of viewpoints within the same topic. Secondly, the topic correlation coefficient is introduced to describe the correlation among topics in different dimensions, and the individual topic support degree is used to measure the influence of topics in different dimensions and that of information from external intervention on individual attitudes. Thirdly, a multi-dimensional public opinion polarization model is constructed by further integrating multi-dimensional attitude interaction rules. Finally, the influence of individual participation, topic status, topic correlation coefficient and external intervention information on the multi-dimensional public opinion polarization process is analyzed through simulation experiments. The simulation results show that: (1) when there is a negative correlation between multi-dimensional topics, as the number of participants on different dimensional topics becomes more consistent, the conflict between multi-dimensional topics will weaken the polarization effect of overall public opinion. However, the effect of public opinion polarization will be enhanced alongwith the enhancement in the confidence of individual opinions. (2) The intervention of external intervention information in different dimensions at different times will further form a multi-dimensional and multi-stage public opinion polarization, and when the multi-dimensional topics are negatively correlated, the intervention of external intervention information will have a stronger impact on the multi-dimensional and multi-stage public opinion polarization process. Finally, the rationality and validity of the proposed model are verified by a real case.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:2:p:472-:d:477026
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/2/472/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/2/472/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Hunt Allcott & Matthew Gentzkow, 2017. "Social Media and Fake News in the 2016 Election," NBER Working Papers 23089, National Bureau of Economic Research, Inc.
    3. Stefan Linde, 2020. "The Politicization of Risk: Party Cues, Polarization, and Public Perceptions of Climate Change Risk," Risk Analysis, John Wiley & Sons, vol. 40(10), pages 2002-2018, October.
    4. Lawrence Blume & Steven Durlauf, 2003. "Equilibrium Concepts for Social Interaction Models," International Game Theory Review (IGTR), World Scientific Publishing Co. Pte. Ltd., vol. 5(03), pages 193-209.
    5. 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.
    6. Zhu, Hou & Hu, Bin, 2018. "Impact of information on public opinion reversal—An agent based model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 578-587.
    7. Löwe, Matthias & Schubert, Kristina & Vermet, Franck, 2020. "Multi-group binary choice with social interaction and a random communication structure—A random graph approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 556(C).
    8. Rama Cont & Matthias Loewe, 2010. "Social distance, heterogeneity and social interactions," Post-Print hal-00545746, HAL.
    9. Cass Sunstein, 2008. "Neither Hayek nor Habermas," Public Choice, Springer, vol. 134(1), pages 87-95, January.
    10. Cont, Rama & Löwe, Matthias, 2010. "Social distance, heterogeneity and social interactions," Journal of Mathematical Economics, Elsevier, vol. 46(4), pages 572-590, July.
    11. Hunt Allcott & Matthew Gentzkow, 2017. "Social Media and Fake News in the 2016 Election," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 211-236, Spring.
    12. 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.
    13. William Pearson, 1966. "Estimation of a correlation coefficient from an uncertainty measure," Psychometrika, Springer;The Psychometric Society, vol. 31(3), pages 421-433, September.
    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. Chenyu Zhang & Jiayue Jiang & Hong Jin & Tinggui Chen, 2021. "The Impact of COVID-19 on Consumers’ Psychological Behavior Based on Data Mining for Online User Comments in the Catering Industry in China," IJERPH, MDPI, vol. 18(8), pages 1-19, April.

    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. 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.
    2. Julia Cage & Nicolas Hervé & Marie-Luce Viaud, 2017. "The Production of Information in an Online World: Is Copy Right?," Working Papers hal-03393171, HAL.
    3. Leopoldo Fergusson & Carlos Molina, 2020. "Facebook Causes Protests," HiCN Working Papers 323, Households in Conflict Network.
    4. Tetsuro Kobayashi & Fumiaki Taka & Takahisa Suzuki, 2021. "Can “Googling” correct misbelief? Cognitive and affective consequences of online search," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-16, September.
    5. Dean Neu & Gregory D. Saxton & Abu S. Rahaman, 2022. "Social Accountability, Ethics, and the Occupy Wall Street Protests," Journal of Business Ethics, Springer, vol. 180(1), pages 17-31, September.
    6. Robbett, Andrea & Matthews, Peter Hans, 2018. "Partisan bias and expressive voting," Journal of Public Economics, Elsevier, vol. 157(C), pages 107-120.
    7. Henrik Skaug Sætra, 2021. "AI in Context and the Sustainable Development Goals: Factoring in the Unsustainability of the Sociotechnical System," Sustainability, MDPI, vol. 13(4), pages 1-19, February.
    8. Fathey Mohammed & Nabil Hasan Al-Kumaim & Ahmed Ibrahim Alzahrani & Yousef Fazea, 2023. "The Impact of Social Media Shared Health Content on Protective Behavior against COVID-19," IJERPH, MDPI, vol. 20(3), pages 1-16, January.
    9. Michele Cantarella & Nicolo' Fraccaroli & Roberto Volpe, 2019. "Does fake news affect voting behaviour?," Department of Economics 0146, University of Modena and Reggio E., Faculty of Economics "Marco Biagi".
    10. Joël Cariolle & Yasmine Elkhateeb & Mathilde Maurel, 2022. "(Mis-)information technology: Internet use and perception of democracy in Africa," Documents de travail du Centre d'Economie de la Sorbonne 22010, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    11. Kerim Peren Arin & Juan A. Lacomba & Francisco Lagos & Deni Mazrekaj & Marcel Thum, 2021. "Misperceptions and Fake News during the Covid-19 Pandemic," CESifo Working Paper Series 9066, CESifo.
    12. Bartosz Wilczek, 2020. "Misinformation and herd behavior in media markets: A cross-national investigation of how tabloids’ attention to misinformation drives broadsheets’ attention to misinformation in political and business," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-22, November.
    13. Barrera, Oscar & Guriev, Sergei & Henry, Emeric & Zhuravskaya, Ekaterina, 2020. "Facts, alternative facts, and fact checking in times of post-truth politics," Journal of Public Economics, Elsevier, vol. 182(C).
    14. Sumeet Kumar & Binxuan Huang & Ramon Alfonso Villa Cox & Kathleen M. Carley, 2021. "An anatomical comparison of fake-news and trusted-news sharing pattern on Twitter," Computational and Mathematical Organization Theory, Springer, vol. 27(2), pages 109-133, June.
    15. Julia Cagé & Nicolas Hervé & Marie-Luce Viaud, 2020. "The Production of Information in an Online World," Review of Economic Studies, Oxford University Press, vol. 87(5), pages 2126-2164.
    16. Zazli Lily Wisker & Robert Neil McKie, 2021. "The effect of fake news on anger and negative word-of-mouth: moderating roles of religiosity and conservatism," Journal of Marketing Analytics, Palgrave Macmillan, vol. 9(2), pages 144-153, June.
    17. Roger D. Magarey & Christina M. Trexler, 2020. "Information: a missing component in understanding and mitigating social epidemics," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-11, December.
    18. Denter, Philipp & Ginzburg, Boris, 2021. "Troll Farms and Voter Disinformation," MPRA Paper 109634, University Library of Munich, Germany.
    19. Christoph March & Ina Schieferdecker, 2021. "Technological Sovereignty as Ability, Not Autarky," CESifo Working Paper Series 9139, CESifo.
    20. Larsen, Vegard H. & Thorsrud, Leif Anders & Zhulanova, Julia, 2021. "News-driven inflation expectations and information rigidities," Journal of Monetary Economics, Elsevier, vol. 117(C), pages 507-520.

    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:18:y:2021:i:2:p:472-:d:477026. 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.