IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i6p914-d1360426.html
   My bibliography  Save this article

A Multi-Information Dissemination Model Based on Cellular Automata

Author

Listed:
  • Changheng Shao

    (College of Computer Science and Technology, Qingdao University, Qingdao 266071, China)

  • Fengjing Shao

    (College of Computer Science and Technology, Qingdao University, Qingdao 266071, China)

  • Xin Liu

    (College of Computer Science and Technology, China University of Petroleum, Qingdao 266580, China)

  • Dawei Yang

    (College of Computer Science and Technology, China University of Petroleum, Qingdao 266580, China)

  • Rencheng Sun

    (College of Computer Science and Technology, Qingdao University, Qingdao 266071, China)

  • Lili Zhang

    (College of Computer Science and Technology, Qingdao University, Qingdao 266071, China)

  • Kaiwen Jiang

    (College of Computer Science and Technology, Qingdao University, Qingdao 266071, China)

Abstract

Significant public opinion events often trigger pronounced fluctuations in online discourse. While existing models have been extensively employed to analyze the propagation of public opinion, they frequently overlook the intricacies of information dissemination among heterogeneous users. To comprehensively address the implications of public opinion outbreaks, it is crucial to accurately predict the evolutionary trajectories of such events, considering the dynamic interplay of multiple information streams. In this study, we propose a SEInR model based on cellular automata to simulate the propagation dynamics of multi-information. By delineating information dissemination rules that govern the diverse modes of information propagation within the network, we achieve precise forecasts of public opinion trends. Through the concurrent simulation and prediction of multi-information game and evolution processes, employing Weibo users as nodes to construct a public opinion cellular automaton, our experimental analysis reveals a significant similarity exceeding 98% between the proposed model and the actual user participation curve observed on the Weibo platform.

Suggested Citation

  • Changheng Shao & Fengjing Shao & Xin Liu & Dawei Yang & Rencheng Sun & Lili Zhang & Kaiwen Jiang, 2024. "A Multi-Information Dissemination Model Based on Cellular Automata," Mathematics, MDPI, vol. 12(6), pages 1-17, March.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:6:p:914-:d:1360426
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/6/914/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/12/6/914/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xiao, Yunpeng & Zhang, Li & Li, Qian & Liu, Ling, 2019. "MM-SIS: Model for multiple information spreading in multiplex network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 135-146.
    2. Sun, Zhuo & Chen, Zhonglong & Hu, Hongtao & Zheng, Jianfeng, 2015. "Ship interaction in narrow water channels: A two-lane cellular automata approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 431(C), pages 46-51.
    3. Yongcong Luo & Jing Ma, 2018. "The influence of positive news on rumor spreading in social networks with scale-free characteristics," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 29(09), pages 1-13, September.
    4. Qi, Le & Zheng, Zhongyi & Gang, Longhui, 2017. "Marine traffic model based on cellular automaton: Considering the change of the ship’s velocity under the influence of the weather and sea," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 483(C), pages 480-494.
    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. Sameera Horawalavithana & Nazim Choudhury & John Skvoretz & Adriana Iamnitchi, 2022. "Online discussion threads as conversation pools: predicting the growth of discussion threads on reddit," Computational and Mathematical Organization Theory, Springer, vol. 28(2), pages 112-140, June.
    2. Zhang, Jing & Wang, Xiaoli & Xie, Yanxi & Wang, Meihua, 2022. "Research on multi-topic network public opinion propagation model with time delay in emergencies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    3. Zhao, Jiansen & Yan, Zhongwei & Chen, Xinqiang & Han, Bing & Wu, Shubo & Ke, Ranxuan, 2022. "k-GCN-LSTM: A k-hop Graph Convolutional Network and Long–Short-Term Memory for ship speed prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    4. Qi, Le & Zheng, Zhongyi & Gang, Longhui, 2017. "A cellular automaton model for ship traffic flow in waterways," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 705-717.
    5. Qi, Le & Zheng, Zhongyi & Gang, Longhui, 2017. "Marine traffic model based on cellular automaton: Considering the change of the ship’s velocity under the influence of the weather and sea," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 483(C), pages 480-494.
    6. Jin, Cheng-Jie & Yang, Wenzhang & Jiang, Rui & Liao, Peng & Zheng, Shiteng & Wang, Hao, 2023. "Vessel-following dynamics: Experiment and modeling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 615(C).
    7. Yao, Yao & Li, Yuanyuan & Xiong, Xi & Wu, Yue & Lin, Honggang & Ju, Shenggen, 2020. "An interactive propagation model of multiple information in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    8. Tavasoli, Ali & Fazli, Mehrdad & Ardjmand, Ehsan & Young, William A. & Shakeri, Heman, 2023. "Competitive pricing under local network effects," European Journal of Operational Research, Elsevier, vol. 311(2), pages 545-566.

    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:6:p:914-:d:1360426. 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.