IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i16p2457-d1452601.html
   My bibliography  Save this article

A Mathematical Optimization Model Designed to Determine the Optimal Timing of Online Rumor Intervention Based on Uncertainty Theory

Author

Listed:
  • Meiling Jin

    (School of Management, Shanghai University, Shanghai 200444, China)

  • Fengming Liu

    (Business School, Shandong Normal University, Jinan 250014, China)

  • Yufu Ning

    (School of Information Engineering, Shandong Youth University of Political Science, Jinan 250103, China
    Smart Healthcare Big Data Engineering and Ubiquitous Computing Characteristic Laboratory in Universities of Shandong, Jinan 250103, China)

  • Yichang Gao

    (Artificial Intelligence and Cyber Futures Institute, Charles Sturt University, Bathurst, NSW 2795, Australia)

  • Dongmei Li

    (School of Management, Shanghai University, Shanghai 200444, China
    School of Economics and Management, Shanghai Zhongqiao Vocational and Technical University, Shanghai 201514, China)

Abstract

The multifaceted nature of online rumors poses challenges to their identification and control. Current approaches to online rumor governance are evolving from fragmented management to collaborative efforts, emphasizing the proactive management of rumor propagation processes. This transformation considers diverse rumor types, the response behaviors of self-media and netizens, and the capabilities of regulatory bodies. This study proposes a multi-agent intervention model rooted in uncertainty theory to mitigate online rumor dissemination. Its empirical validation includes comparing three rumor categories and testing it against a single-agent model, highlighting the efficacy of collaborative governance. Quantitative assessments underscore the model’s utility in providing regulatory authorities with a robust theoretical framework for adaptive decision-making and strategy adjustments based on real-world conditions.

Suggested Citation

  • Meiling Jin & Fengming Liu & Yufu Ning & Yichang Gao & Dongmei Li, 2024. "A Mathematical Optimization Model Designed to Determine the Optimal Timing of Online Rumor Intervention Based on Uncertainty Theory," Mathematics, MDPI, vol. 12(16), pages 1-21, August.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:16:p:2457-:d:1452601
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/16/2457/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/12/16/2457/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Huo, Liang’an & Ma, Chenyang, 2017. "The interaction evolution model of mass incidents with delay in a social network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 484(C), pages 440-452.
    2. Tingqing Ye & Baoding Liu, 2023. "Uncertain hypothesis test for uncertain differential equations," Fuzzy Optimization and Decision Making, Springer, vol. 22(2), pages 195-211, June.
    3. Jinwu Gao & Zhuolin Cui & Huijie Li & Ruru Jia, 2023. "Optimization and Coordination of the Fresh Agricultural Product Supply Chain Considering the Freshness-Keeping Effort and Information Sharing," Mathematics, MDPI, vol. 11(8), pages 1-25, April.
    4. Bodaghi, Amirhosein & Goliaei, Sama & Salehi, Mostafa, 2019. "The number of followings as an influential factor in rumor spreading," Applied Mathematics and Computation, Elsevier, vol. 357(C), pages 167-184.
    5. Li, Bo & Huang, Yayi, 2023. "Uncertain random portfolio selection with different mental accounts based on mixed data," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    6. Liu, Z., 2021. "Generalized moment estimation for uncertain differential equations," Applied Mathematics and Computation, Elsevier, vol. 392(C).
    7. Yang, Xiangfeng & Liu, Yuhan & Park, Gyei-Kark, 2020. "Parameter estimation of uncertain differential equation with application to financial market," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    Full references (including those not matched with items on IDEAS)

    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. He, Liu & Zhu, Yuanguo, 2024. "Nonparametric estimation for uncertain fractional differential equations," Chaos, Solitons & Fractals, Elsevier, vol. 178(C).
    2. Shen, Jiayu & Shi, Jianxin & Gao, Lingceng & Zhang, Qiang & Zhu, Kai, 2023. "Uncertain green product supply chain with government intervention," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 208(C), pages 136-156.
    3. Liu, Hanjie & Zhu, Yuanguo, 2024. "Carbon option pricing based on uncertain fractional differential equation: A binomial tree approach," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 225(C), pages 13-28.
    4. Liu, Z. & Yang, Y., 2021. "Selection of uncertain differential equations using cross validation," Chaos, Solitons & Fractals, Elsevier, vol. 148(C).
    5. Zhang, Guidong & Sheng, Yuhong, 2022. "Estimating time-varying parameters in uncertain differential equations," Applied Mathematics and Computation, Elsevier, vol. 425(C).
    6. Tang, Han & Yang, Xiangfeng, 2022. "Moment estimation in uncertain differential equations based on the Milstein scheme," Applied Mathematics and Computation, Elsevier, vol. 418(C).
    7. Waichon Lio & Rui Kang, 2023. "Bayesian rule in the framework of uncertainty theory," Fuzzy Optimization and Decision Making, Springer, vol. 22(3), pages 337-358, September.
    8. Noorani, Idin & Mehrdoust, Farshid, 2022. "Parameter estimation of uncertain differential equation by implementing an optimized artificial neural network," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
    9. Wang, Weiwei & Ralescu, Dan A., 2021. "Valuation of lookback option under uncertain volatility model," Chaos, Solitons & Fractals, Elsevier, vol. 153(P1).
    10. Lifen Jia & Linya Zhang & Wei Chen, 2024. "China’s carbon emission allowance prices forecasting and option designing in uncertain environment," Fuzzy Optimization and Decision Making, Springer, vol. 23(4), pages 539-560, December.
    11. Xie, Jinsheng & Lio, Waichon & Kang, Rui, 2024. "Analysis of simple pendulum with uncertain differential equation," Chaos, Solitons & Fractals, Elsevier, vol. 185(C).
    12. Kaixi Zhang & Baoding Liu, 2024. "Higher-order derivative of uncertain process and higher-order uncertain differential equation," Fuzzy Optimization and Decision Making, Springer, vol. 23(2), pages 295-318, June.
    13. Liu, Z. & Yang, Y., 2021. "Uncertain pharmacokinetic model based on uncertain differential equation," Applied Mathematics and Computation, Elsevier, vol. 404(C).
    14. Yang Liu & Baoding Liu, 2022. "Residual analysis and parameter estimation of uncertain differential equations," Fuzzy Optimization and Decision Making, Springer, vol. 21(4), pages 513-530, December.
    15. Tingqing Ye & Baoding Liu, 2022. "Uncertain hypothesis test with application to uncertain regression analysis," Fuzzy Optimization and Decision Making, Springer, vol. 21(2), pages 157-174, June.
    16. Liu, Zhe & Yang, Ying, 2022. "Moment estimation for parameters in high-order uncertain differential equations," Applied Mathematics and Computation, Elsevier, vol. 433(C).
    17. Liu He & Yuanguo Zhu & Yajing Gu, 2023. "Nonparametric estimation for uncertain differential equations," Fuzzy Optimization and Decision Making, Springer, vol. 22(4), pages 697-715, December.
    18. Farshid Mehrdoust & Idin Noorani & Wei Xu, 2023. "Uncertain energy model for electricity and gas futures with application in spark-spread option price," Fuzzy Optimization and Decision Making, Springer, vol. 22(1), pages 123-148, March.
    19. Lu, Jing & Yang, Xiangfeng & Tian, Miao, 2022. "Barrier swaption pricing formulae of mean-reverting model in uncertain environment," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    20. Najafi, Alireza & Taleghani, Rahman, 2022. "Fractional Liu uncertain differential equation and its application to finance," Chaos, Solitons & Fractals, Elsevier, vol. 165(P2).

    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:jmathe:v:12:y:2024:i:16:p:2457-:d:1452601. 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.