Research article Special Issues

Rumor model on social networks contemplating self-awareness and saturated transmission rate

  • Received: 12 June 2024 Revised: 08 August 2024 Accepted: 22 August 2024 Published: 02 September 2024
  • MSC : 34A34, 34D05, 34D20

  • The propagation of rumors indisputably inflicts profound negative impacts on society and individuals. This article introduces a new unaware ignorants-aware ignorants-spreaders-recovereds $ (2ISR) $ rumor spreading model that combines individual vigilance self-awareness with nonlinear spreading rate. Initially, the positivity of the system solutions and the existence of its positive invariant set are rigorously proved, and the rumor propagation threshold is solved using the next-generation matrix method. Next, a comprehensive analysis is conducted on the existence of equilibrium points of the system and the occurrence of backward bifurcation. Afterward, the stability of the system is validated at both the rumor-free equilibrium and the rumor equilibrium, employing the Jacobian matrix approach as well as the Lyapunov stability theory. To enhance the efficacy of rumor propagation management, a targeted optimal control strategy is formulated, drawing upon the Pontryagin's Maximum principle as a guiding framework. Finally, through sensitivity analyses, numerical simulations, and tests of real cases, we verify the reliability of the theoretical results and further consolidate the solid foundation of the above theoretical arguments.

    Citation: Hui Wang, Shuzhen Yu, Haijun Jiang. Rumor model on social networks contemplating self-awareness and saturated transmission rate[J]. AIMS Mathematics, 2024, 9(9): 25513-25531. doi: 10.3934/math.20241246

    Related Papers:

  • The propagation of rumors indisputably inflicts profound negative impacts on society and individuals. This article introduces a new unaware ignorants-aware ignorants-spreaders-recovereds $ (2ISR) $ rumor spreading model that combines individual vigilance self-awareness with nonlinear spreading rate. Initially, the positivity of the system solutions and the existence of its positive invariant set are rigorously proved, and the rumor propagation threshold is solved using the next-generation matrix method. Next, a comprehensive analysis is conducted on the existence of equilibrium points of the system and the occurrence of backward bifurcation. Afterward, the stability of the system is validated at both the rumor-free equilibrium and the rumor equilibrium, employing the Jacobian matrix approach as well as the Lyapunov stability theory. To enhance the efficacy of rumor propagation management, a targeted optimal control strategy is formulated, drawing upon the Pontryagin's Maximum principle as a guiding framework. Finally, through sensitivity analyses, numerical simulations, and tests of real cases, we verify the reliability of the theoretical results and further consolidate the solid foundation of the above theoretical arguments.



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    [1] D. J. Daley, D. G. Kendall, Epidemics and rumours, Nature, 204 (1964), 1118. https://doi.org/10.1038/2041118a0 doi: 10.1038/2041118a0
    [2] D. J. Daley, D. G. Kendall, Stochastic rumours, IMA J. Appl. Math., 1 (1965), 42–55. https://doi.org/10.1093/imamat/1.1.42 doi: 10.1093/imamat/1.1.42
    [3] D. P. Maki, Mathematical models and applications, with emphasis on social, life, and management sciences, Upper Saddle River, NJ, USA: Prentice-Hall, 1973.
    [4] [ 10.1038/30918] D. J. Watts, S. H. Strogatz, Collective dynamics of'small-world'networks, Nature, 393 (1998), 440–442. https://doi.org/10.1038/30918 doi: 10.1038/30918
    [5] M. E. J. Newman, D. J. Watts, Scaling and percolation in the small-world network model, Phys. Rev. E, 60 (1999), 7332. https://doi.org/10.1103/physreve.60.7332 doi: 10.1103/physreve.60.7332
    [6] A. L. Barabsi, R. Albert, H. Jeong, Mean-field theory for scale-free random networks, Phys. A, 272 (1999), 173–187. https://doi.org/10.1016/s0378-4371(99)00291-5 doi: 10.1016/s0378-4371(99)00291-5
    [7] R. Sun, W. B. Luo, Rumor propagation model for complex network with non-uniform propagation rates, Appl. Mech. Mater., 596 (2014), 868–872. https://doi.org/10.4028/www.scientific.net/amm.596.868 doi: 10.4028/www.scientific.net/amm.596.868
    [8] K. Ji, J. Liu, G. Xiang, Anti-rumor dynamics and emergence of the timing threshold on complex network, Phys. A, 411 (2014), 87–94. https://doi.org/10.1016/j.physa.2014.06.013 doi: 10.1016/j.physa.2014.06.013
    [9] N. Ding, G. Guan, S. Shen, L. Zhu, Dynamical behaviors and optimal control of delayed S2IS rumor propagation model with saturated conversion function over complex networks, Commun. Nonlinear Sci., 128 (2024), 107603. https://doi.org/10.1016/j.cnsns.2023.107603 doi: 10.1016/j.cnsns.2023.107603
    [10] D. Li, J. Ma, Z. Tian, H. Zhu, An evolutionary game for the diffusion of rumor in complex networks, Phys. A, 433 (2015), 51–58. https://doi.org/10.1016/j.physa.2015.03.080 doi: 10.1016/j.physa.2015.03.080
    [11] X. Zhang, Y. Zhang, T. Lv, Y. Yin, Identification of efficient observers for locating spreading source in complex networks, Phys. A, 442 (2016), 100–109. https://doi.org/10.1016/j.physa.2015.09.017 doi: 10.1016/j.physa.2015.09.017
    [12] Y. Moreno, M. Nekovee, A. F. Pacheco, Dynamics of rumor spreading in complex networks, Phys. Rev. E, 69 (2004), 066130. https://doi.org/10.1103/physreve.69.066130 doi: 10.1103/physreve.69.066130
    [13] Y. Yao, X. Xiao, C. Zhang, C. Dou, S. Xia, Stability analysis of an SDILR model based on rumor recurrence on social media, Phys. A, 535 (2019), 122236. https://doi.org/10.1016/j.physa.2019.122236 doi: 10.1016/j.physa.2019.122236
    [14] J. Chen, L. X. Yang, X. Yang, Y. Y. Tang, Cost-effective anti-rumor message-pushing schemes, Phys. A, 540 (2020), 123085. https://doi.org/10.1016/j.physa.2019.123085 doi: 10.1016/j.physa.2019.123085
    [15] X. Chen, N. Wang, Rumor spreading model considering rumor credibility, correlation and crowd classification based on personality, Sci. Rep., 10 (2020), 5887. https://doi.org/10.1038/s41598-020-62585-9 doi: 10.1038/s41598-020-62585-9
    [16] T. Li, Y. B. Liu, X. H. Wu, Y. P. Xiao, C. Y. Sang, Dynamic model of Malware propagation based on tripartite graph and spread influence, Nonlinear Dyn., 101 (2020), 2671–2686. https://doi.org/10.1007/s11071-020-05935-6 doi: 10.1007/s11071-020-05935-6
    [17] A. M. Al-Oraiqat, O. S. Ulichev, Y. V. Meleshko, H. S. AlRawashdeh, O. O. Smirnov, L. I. Polishchuk, Modeling strategies for information influence dissemination in social networks, J. Amb. Intel. Hum. Comp., 13 (2022), 2463–2477. https://doi.org/10.1007/s12652-021-03364-w doi: 10.1007/s12652-021-03364-w
    [18] A. Jain, J. Dhar, V. K. Gupta, Optimal control of rumor spreading model on homogeneous social network with consideration of influence delay of thinkers, Differ. Equ. Dyn. Syst., 31 (2023), 113–134. https://doi.org/10.1007/s12591-019-00484-w doi: 10.1007/s12591-019-00484-w
    [19] L. A. Huo, L. Wang, X. M. Zhao, Stability analysis and optimal control of a rumor spreading model with media report, Phys. A, 517 (2019), 551–562. https://doi.org/10.1016/j.physa.2018.11.047 doi: 10.1016/j.physa.2018.11.047
    [20] M. Ghosh, S. Das, P. Das, Dynamics and control of delayed rumor propagation through social networks, J. Appl. Math. Comput., 68 (2022), 3011–3040. https://doi.org/10.1007/s12190-021-01643-5 doi: 10.1007/s12190-021-01643-5
    [21] P. Van den Driessche, J. Watmough, Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission, Math. Biosci., 180 (2002), 29–48. https://doi.org/10.1016/s0025-5564(02)00108-6 doi: 10.1016/s0025-5564(02)00108-6
    [22] A. Hurwitz, Ueber die Bedingungen, unter welchen eine Gleichung nur Wurzeln mit negativen reellen Theilen besitzt, Math. Ann., 46 (1895), 273–284. https://doi.org/10.1007/bf01446812 doi: 10.1007/bf01446812
    [23] D. Grass, J. P. Caulkins, G. Feichtinger, G. Tragler, D. A. Behrens, Optimal Control of Nonlinear Processes, Berlin: Springer, 2008. https://doi.org/10.1007/978-3-540-77647-5
    [24] N. Chitnis, J. M. Hyman, J. M. Cushing, Determining important parameters in the spread of malaria through the sensitivity analysis of a mathematical model, Bull. Math. Biol., 70 (2008), 1272–1296. https://doi.org/10.1007/s11538-008-9299-0 doi: 10.1007/s11538-008-9299-0
    [25] S. Z. Yu, Z. Y. Yu, H. J. Jiang, S. Yang, The dynamics and control of 2I2SR rumor spreading models in multilingual online social networks, Inform. Sci., 581 (2021), 18–41. https://doi.org/10.1016/j.ins.2021.08.096 doi: 10.1016/j.ins.2021.08.096
    [26] J. R. Li, H. J. Jiang, X. H. Mei, C. Hu, G. L. Zhang, Dynamical analysis of rumor spreading model in multi-lingual environment and heterogeneous complex networks, Inform. Sci., 536 (2020), 391–408. https://doi.org/10.1016/j.ins.2020.05.037 doi: 10.1016/j.ins.2020.05.037
    [27] J. F. Wang, L. X. Tian, Z. L. Zhen, Global Lagrange stability for Takagi-Sugeno fuzzy Cohen-Grossberg BAM neural networks with time-varying delays, Int. J. Control Autom. Syst., 16 (2018), 1603–1614. https://doi.org/10.1007/s12555-017-0618-9 doi: 10.1007/s12555-017-0618-9
    [28] F. Y. Zhou, H. X. Yao, Stability analysis for neutral-type inertial BAM neural networks with time-varying delays, Nonlinear Dyn., 92 (2018), 1583–1598. https://doi.org/10.1007/s11071-018-4148-7 doi: 10.1007/s11071-018-4148-7
    [29] L. A. Huo, X. M. Chen, L. J. Zhao, The optimal event-triggered impulsive control of a stochastic rumor spreading model incorporating time delay using the particle swarm optimization algorithm, J. Franklin Inst., 360 (2023), 4695–4718. https://doi.org/10.1016/j.jfranklin.2023.03.006 doi: 10.1016/j.jfranklin.2023.03.006
    [30] S. Z. Yu, Z. Y. Yu, H. J. Jiang, A rumor propagation model in multilingual environment with time and state dependent impulsive control, Chaos Solitons Fract., 182 (2024), 114779. https://doi.org/10.1016/j.chaos.2024.114779 doi: 10.1016/j.chaos.2024.114779
    [31] X. R. Tong, H. J. Jiang, X. Y. Chen, J. R. Li, Z. Cao, Anosov flows with stable and unstable differentiable distributions, Math. Meth. Appl. Sci., 46 (2023), 7125–7139. https://doi.org/10.1002/mma.8959 doi: 10.1002/mma.8959
    [32] K. Myilsamy, M. S. Kuma, A. S. Kumar, Optimal control of a stochastic rumour propagation in online social networks, Int. J. Mod. Phys. C, 34 (2023), 1–20. https://doi.org/10.1142/s0129183123501620 doi: 10.1142/s0129183123501620
    [33] Y. H. Zhang, J. J. Zhu, A. Din, X. C. Ma, Dynamics of a stochastic epidemic-like rumor propagation model with generalized nonlinear incidence and time delay, Phys. Scr., 98 (2023), 045232. https://doi.org/10.1088/1402-4896/acc558 doi: 10.1088/1402-4896/acc558
    [34] S. D. Kang, X. L. Hou, Y. H. Hu, H. Y. Liu, Dynamic analysis and optimal control of a stochastic information spreading model considering super-spreader and implicit exposer with random parametric perturbations, Front. Phys., 11 (2023), 1194804. https://doi.org/10.3389/fphy.2023.1194804 doi: 10.3389/fphy.2023.1194804
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