IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i6p989-d774822.html
   My bibliography  Save this article

Description of the Distribution Law and Non-Linear Dynamics of Growth of Comments Number in News and Blogs Based on the Fokker-Planck Equation

Author

Listed:
  • Dmitry Zhukov

    (Institute of Cybersecurity and Digital Technologies, MIREA-Russian Technological University, 78 Vernadsky Avenue, 119454 Moscow, Russia)

  • Julia Perova

    (Institute of Radio Electronics and Computer Science, MIREA-Russian Technological University, 78 Vernadsky Avenue, 119454 Moscow, Russia)

  • Vladimir Kalinin

    (Institute of Cybersecurity and Digital Technologies, MIREA-Russian Technological University, 78 Vernadsky Avenue, 119454 Moscow, Russia)

Abstract

The article considers stationary and dynamic distributions of news by the number of comments. The processing of the observed data showed that static distribution of news by the number of comments relating to that news obeys a power law, and the dynamic distribution (the change in number of comments over time) in some cases has an S-shaped character, and in some cases a more complex two-stage character. This depends on the time interval between the appearance of a comment at the first level and a comment attached to that comment. The power law for the stationary probability density of news distribution by the number of comments can be obtained from the solution of the stationary Fokker-Planck equation, if a number of assumptions are made in its derivation. In particular, we assume that the drift coefficient μ ( x ) responsible in the Fokker-Planck equation for a purposeful change in the state of system x ( x is the current number of comments on that piece of news) linearly depends on the state x , and the diffusion coefficient D ( x ) responsible for a random change depends quadratically on x . The solution of the unsteady Fokker-Planck differential equation with these assumptions made it possible to obtain an analytical equation for the probability density of transitions between the states of the system per unit of time, which is in good agreement with the observed data, considering the effect of the delay time between the appearance of the first-level comment and the comment on that comment.

Suggested Citation

  • Dmitry Zhukov & Julia Perova & Vladimir Kalinin, 2022. "Description of the Distribution Law and Non-Linear Dynamics of Growth of Comments Number in News and Blogs Based on the Fokker-Planck Equation," Mathematics, MDPI, vol. 10(6), pages 1-24, March.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:6:p:989-:d:774822
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/6/989/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/10/6/989/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Liu, Xiaoyang & He, Daobing & Liu, Chao, 2018. "Modeling information dissemination and evolution in time-varying online social network based on thermal diffusion motion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 456-476.
    2. Lux, Thomas, 2012. "Inference for systems of stochastic differential equations from discretely sampled data: A numerical maximum likelihood approach," Kiel Working Papers 1781, Kiel Institute for the World Economy (IfW Kiel).
    3. Dmitry Zhukov & Tatiana Khvatova & Carla Millar & Anastasia Zaltcman, 2020. "Modelling the stochastic dynamics of transitions between states in social systems incorporating self-organization and memory," Post-Print hal-03188186, HAL.
    4. Sahafizadeh, Ebrahim & Tork Ladani, Behrouz, 2018. "The impact of group propagation on rumor spreading in mobile social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 412-423.
    5. Zhukov, Dmitry & Khvatova, Tatiana & Millar, Carla & Zaltcman, Anastasia, 2020. "Modelling the stochastic dynamics of transitions between states in social systems incorporating self-organization and memory," Technological Forecasting and Social Change, Elsevier, vol. 158(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. Zhao, Jianyu & Yu, Lean & Xi, Xi & Li, Shengliang, 2023. "Knowledge percolation threshold and optimization strategies of the combinatorial network for complex innovation in the digital economy," Omega, Elsevier, vol. 120(C).
    2. Zhukov, Dmitry & Khvatova, Tatiana & Millar, Carla & Andrianova, Elena, 2022. "Beyond big data – new techniques for forecasting elections using stochastic models with self-organisation and memory," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    3. Wu, Zhonghuan & Duan, Chunlin & Cui, Yuting & Qin, Rong, 2023. "Consumers' attitudes toward low-carbon consumption based on a computational model: Evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 186(PA).
    4. Yin, Fulian & Jiang, Xinyi & Qian, Xiqing & Xia, Xinyu & Pan, Yanyan & Wu, Jianhong, 2022. "Modeling and quantifying the influence of rumor and counter-rumor on information propagation dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    5. Lu, Peng, 2019. "Heterogeneity, judgment, and social trust of agents in rumor spreading," Applied Mathematics and Computation, Elsevier, vol. 350(C), pages 447-461.
    6. Lu, Peng & Deng, Liping & Liao, Hongbing, 2019. "Conditional effects of individual judgment heterogeneity in information dissemination," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 335-344.
    7. Cheng, Yingying & Huo, Liang'an & Zhao, Laijun, 2022. "Stability analysis and optimal control of rumor spreading model under media coverage considering time delay and pulse vaccination," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
    8. Sang, Chun-Yan & Liao, Shi-Gen, 2020. "Modeling and simulation of information dissemination model considering user’s awareness behavior in mobile social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    9. Lan, Yuexin & Lian, Zhixuan & Zeng, Runxi & Zhu, Di & Xia, Yixue & Liu, Mo & Zhang, Peng, 2020. "A statistical model of the impact of online rumors on the information quantity of online public opinion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    10. esposito, francesco paolo & cummins, mark, 2015. "Filtering and likelihood estimation of latent factor jump-diffusions with an application to stochastic volatility models," MPRA Paper 64987, University Library of Munich, Germany.
    11. Sahafizadeh, Ebrahim & Tork Ladani, Behrouz, 2023. "Soft rumor control in mobile instant messengers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    12. Zhukov, Dmitry & Khvatova, Tatiana & Millar, Carla & Zaltcman, Anastasia, 2020. "Modelling the stochastic dynamics of transitions between states in social systems incorporating self-organization and memory," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    13. Lu, Peng & Yao, Qi & Lu, Pengfei, 2019. "Two-stage predictions of evolutionary dynamics during the rumor dissemination," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 517(C), pages 349-369.
    14. 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.
    15. Askarizadeh, Mojgan & Tork Ladani, Behrouz & Manshaei, Mohammad Hossein, 2019. "An evolutionary game model for analysis of rumor propagation and control in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 21-39.
    16. Yin, Fulian & Xia, Xinyu & Zhang, Xiaojian & Zhang, Mingjia & Lv, Jiahui & Wu, Jianhong, 2021. "Modelling the dynamic emotional information propagation and guiding the public sentiment in the Chinese Sina-microblog," Applied Mathematics and Computation, Elsevier, vol. 396(C).

    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:10:y:2022:i:6:p:989-:d:774822. 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.