IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v564y2021ics0378437120307998.html
   My bibliography  Save this article

Measuring network rationality and simulating information diffusion based on network structure

Author

Listed:
  • Gong, Hao
  • Guo, Chunxiang
  • Liu, Yu

Abstract

The absence of rationality in online public discussions leads to many negative problems, such as cyber violence, which often causes serious psychological harm to the people involved in public opinions. The participants in online discussions constitute a complex network system; however, until now, there are few studies that have proposed a definition and measurement of network rationality from a systematic perspective, which makes us unable to assess the rationality of a network reasonably. In this paper, we propose a definition and measurement of network rationality. Then, we build a model based on the fraction of rational nodes and homophily to simulate the generation of social networks, and we reveal their influence on network rationality and irrational information diffusion on a network. Our preliminary results show that a network with higher rationality will create a ”virtuous circle” more easily than a network with lower rationality, even if they have exactly the same initial number of rational nodes. In addition, the higher the rationality of a network is, the weaker the influence of the irrational information on the network will be.

Suggested Citation

  • Gong, Hao & Guo, Chunxiang & Liu, Yu, 2021. "Measuring network rationality and simulating information diffusion based on network structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 564(C).
  • Handle: RePEc:eee:phsmap:v:564:y:2021:i:c:s0378437120307998
    DOI: 10.1016/j.physa.2020.125501
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437120307998
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

    File URL: https://libkey.io/10.1016/j.physa.2020.125501?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Fortunato, Santo, 2005. "Damage spreading and opinion dynamics on scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 348(C), pages 683-690.
    2. Chrysanthos Dellarocas, 2006. "Strategic Manipulation of Internet Opinion Forums: Implications for Consumers and Firms," Management Science, INFORMS, vol. 52(10), pages 1577-1593, October.
    3. Rainer Hegselmann & Ulrich Krause, 2002. "Opinion Dynamics and Bounded Confidence Models, Analysis and Simulation," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 5(3), pages 1-2.
    4. 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.
    5. Renbin Xiao & Tongyang Yu & Jundong Hou, 2020. "Modeling and Simulation of Opinion Natural Reversal Dynamics with Opinion Leader Based on HK Bounded Confidence Model," Complexity, Hindawi, vol. 2020, pages 1-20, March.
    6. Nizamani, Sarwat & Memon, Nasrullah & Galam, Serge, 2014. "From public outrage to the burst of public violence: An epidemic-like model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 620-630.
    7. Liu, Wanping & Wu, Xiao & Yang, Wu & Zhu, Xiaofei & Zhong, Shouming, 2019. "Modeling cyber rumor spreading over mobile social networks: A compartment approach," Applied Mathematics and Computation, Elsevier, vol. 343(C), pages 214-229.
    8. Huang, Chuanchao & Hu, Bin & Jiang, Guoyin & Yang, Ruixian, 2016. "Modeling of agent-based complex network under cyber-violence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 458(C), pages 399-411.
    9. Yi, Yinxue & Zhang, Zufan & Gan, Chenquan, 2018. "The effect of social tie on information diffusion in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 783-794.
    10. Liu, Wenjin & Li, Tao & Cheng, Xinming & Xu, Hao & Liu, Xiongding, 2019. "Spreading dynamics of a cyber violence model on scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 531(C).
    11. Deng, Lei & Liu, Yun & Zeng, Qing-An, 2012. "How information influences an individual opinion evolution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(24), pages 6409-6417.
    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. Yang, Ping & Gao, Xiangyun & Zhao, Yiran & Jia, Nanfei & Dong, Xiaojuan, 2021. "Lithium resource allocation optimization of the lithium trading network based on material flow," Resources Policy, Elsevier, vol. 74(C).

    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. Huang, Chuanchao & Hu, Bin & Jiang, Guoyin & Yang, Ruixian, 2016. "Modeling of agent-based complex network under cyber-violence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 458(C), pages 399-411.
    2. 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.
    3. Jiang, Guoyin & Shang, Jennifer & Liu, Wenping & Feng, Xiaodong & Lei, Junli, 2020. "Modeling the dynamics of online review life cycle: Role of social and economic moderations," European Journal of Operational Research, Elsevier, vol. 285(1), pages 360-379.
    4. Cheng, Zhichao & Xiong, Yang & Xu, Yiwen, 2016. "An opinion diffusion model with decision-making groups: The influence of the opinion’s acceptability," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 429-438.
    5. Buechel, Berno & Hellmann, Tim & Klößner, Stefan, 2015. "Opinion dynamics and wisdom under conformity," Journal of Economic Dynamics and Control, Elsevier, vol. 52(C), pages 240-257.
    6. Heyes, Anthony & Kapur, Sandeep, 2012. "Angry customers, e-word-of-mouth and incentives for quality provision," Journal of Economic Behavior & Organization, Elsevier, vol. 84(3), pages 813-828.
    7. Lu, Xi & Mo, Hongming & Deng, Yong, 2015. "An evidential opinion dynamics model based on heterogeneous social influential power," Chaos, Solitons & Fractals, Elsevier, vol. 73(C), pages 98-107.
    8. Andreas Koulouris & Ioannis Katerelos & Theodore Tsekeris, 2013. "Multi-Equilibria Regulation Agent-Based Model of Opinion Dynamics in Social Networks," Interdisciplinary Description of Complex Systems - scientific journal, Croatian Interdisciplinary Society Provider Homepage: http://indecs.eu, vol. 11(1), pages 51-70.
    9. Wu, Xingli & Liao, Huchang, 2021. "Modeling personalized cognition of customers in online shopping," Omega, Elsevier, vol. 104(C).
    10. Khim-Yong Goh & Cheng-Suang Heng & Zhijie Lin, 2013. "Social Media Brand Community and Consumer Behavior: Quantifying the Relative Impact of User- and Marketer-Generated Content," Information Systems Research, INFORMS, vol. 24(1), pages 88-107, March.
    11. Crokidakis, Nuno & Sigaud, Lucas, 2021. "Modeling the evolution of drinking behavior: A Statistical Physics perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 570(C).
    12. Zhang, Yaming & Su, Yanyuan & Weigang, Li & Liu, Haiou, 2019. "Interacting model of rumor propagation and behavior spreading in multiplex networks," Chaos, Solitons & Fractals, Elsevier, vol. 121(C), pages 168-177.
    13. Li, Yin & Arora, Sanjay & Youtie, Jan & Shapira, Philip, 2018. "Using web mining to explore Triple Helix influences on growth in small and mid-size firms," Technovation, Elsevier, vol. 76, pages 3-14.
    14. Thomas Moore & Patrick Finley & Nancy Brodsky & Theresa Brown & Benjamin Apelberg & Bridget Ambrose & Robert Glass, 2015. "Modeling Education and Advertising with Opinion Dynamics," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 18(2), pages 1-7.
    15. George Butler & Gabriella Pigozzi & Juliette Rouchier, 2019. "Mixing Dyadic and Deliberative Opinion Dynamics in an Agent-Based Model of Group Decision-Making," Complexity, Hindawi, vol. 2019, pages 1-31, August.
    16. Guillaume Deffuant & Ilaria Bertazzi & Sylvie Huet, 2018. "The Dark Side Of Gossips: Hints From A Simple Opinion Dynamics Model," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 21(06n07), pages 1-20, September.
    17. G Jordan Maclay & Moody Ahmad, 2021. "An agent based force vector model of social influence that predicts strong polarization in a connected world," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-42, November.
    18. Zhuang, Mengzhou & Cui, Geng & Peng, Ling, 2018. "Manufactured opinions: The effect of manipulating online product reviews," Journal of Business Research, Elsevier, vol. 87(C), pages 24-35.
    19. Tiwari, Mukesh & Yang, Xiguang & Sen, Surajit, 2021. "Modeling the nonlinear effects of opinion kinematics in elections: A simple Ising model with random field based study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 582(C).
    20. Saggau, Volker, 2012. "Viele Köche Verderben Den Brei – Agentenbasierte Simulationen Zum Föderalismusdurcheinander Während Der Ehec-Krise," 52nd Annual Conference, Stuttgart, Germany, September 26-28, 2012 133052, German Association of Agricultural Economists (GEWISOLA).

    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:eee:phsmap:v:564:y:2021:i:c:s0378437120307998. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.