Research article

Modeling and analyzing an opinion network dynamics considering the environmental factor


  • Received: 03 June 2023 Revised: 03 August 2023 Accepted: 11 August 2023 Published: 24 August 2023
  • With the development of Internet technology, social media has gradually become an important platform where users can express opinions about hot events. Research on the mechanism of public opinion evolution is beneficial to guide the trend of opinions, making users' opinions change in a positive direction or reach a consensus among controversial crowds. To design effective strategies for public opinion management, we propose a dynamic opinion network susceptible-forwarding-immune model considering environmental factors (NET-OE-SFI), which divides the forwarding nodes into two types: support and opposition based on the real data of users. The NET-OE-SFI model introduces environmental factors from infectious diseases into the study of network information transmission, which aims to explore the evolution law of users' opinions affected by the environment. We attempt to combine the complex media environmental factors in social networks with users' opinion information to study the influence of environmental factors on the evolution of public opinion. Data fitting of real information transmission data fully demonstrates the validity of this model. We have also made a variety of sensitivity analysis experiments to study the influence of model parameters, contributing to the design of reasonable and effective strategies for public opinion guidance.

    Citation: Fulian Yin, Jinxia Wang, Xinyi Jiang, Yanjing Huang, Qianyi Yang, Jianhong Wu. Modeling and analyzing an opinion network dynamics considering the environmental factor[J]. Mathematical Biosciences and Engineering, 2023, 20(9): 16866-16885. doi: 10.3934/mbe.2023752

    Related Papers:

  • With the development of Internet technology, social media has gradually become an important platform where users can express opinions about hot events. Research on the mechanism of public opinion evolution is beneficial to guide the trend of opinions, making users' opinions change in a positive direction or reach a consensus among controversial crowds. To design effective strategies for public opinion management, we propose a dynamic opinion network susceptible-forwarding-immune model considering environmental factors (NET-OE-SFI), which divides the forwarding nodes into two types: support and opposition based on the real data of users. The NET-OE-SFI model introduces environmental factors from infectious diseases into the study of network information transmission, which aims to explore the evolution law of users' opinions affected by the environment. We attempt to combine the complex media environmental factors in social networks with users' opinion information to study the influence of environmental factors on the evolution of public opinion. Data fitting of real information transmission data fully demonstrates the validity of this model. We have also made a variety of sensitivity analysis experiments to study the influence of model parameters, contributing to the design of reasonable and effective strategies for public opinion guidance.



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    [1] S. Galam, Modelling rumors: the no plane Pentagon French hoax case, Physica A, 320 (2003), 571–580. https://doi.org/10.1016/S0378-4371(02)01582-0 doi: 10.1016/S0378-4371(02)01582-0
    [2] J. Zhou, Z. Liu, B. Li, Influence of network structure on rumor propagation, Phys. Lett. A, 368 (2007), 458–463. https://doi.org/10.1016/j.physleta.2007.01.094 doi: 10.1016/j.physleta.2007.01.094
    [3] B. Zhang, X. Guan, M. J. Khan, Y. Zhou, A time-varying propagation model of hot topic on BBS sites and Blog networks, Inf. Sci., 187 (2012), 15–32. https://doi.org/10.1016/j.ins.2011.09.025 doi: 10.1016/j.ins.2011.09.025
    [4] M. E. J. Newman, Fast algorithm for detecting community structure in networks, Phys. Rev. E, 69 (2004), 066133. https://doi.org/10.1103/PhysRevE.69.066133 doi: 10.1103/PhysRevE.69.066133
    [5] A. Mislove, H. S. Koppula, K. P. Gummadi, P. Druschel, B. Bhattacharjee, Growth of the flickr social network, in Proceedings of the First Workshop on Online Social Networks, (2008), 25–30. https://doi.org/10.1145/1397735.1397742
    [6] Y. Bao, C. Yi, Y. Xue, Y. Dong, Precise modeling rumor propagation and control strategy on social networks, in Applications of Social Media and Social Network Analysis, (2015), 77–102. https://doi.org/10.1007/978-3-319-19003-7_5
    [7] J. Yi, P. Liu, X. Tang, W. Liu, Improved SIR advertising spreading model and its effectiveness in social network, Procedia Comput. Sci., 129 (2018), 215–218. https://doi.org/10.1016/j.procs.2018.03.044 doi: 10.1016/j.procs.2018.03.044
    [8] N. Liu, H. An, X. Gao, H. Li, X. Hao, Breaking news dissemination in the media via propagation behavior based on complex network theory, Physica A, 453 (2016), 44–54. https://doi.org/10.1016/j.physa.2016.02.046 doi: 10.1016/j.physa.2016.02.046
    [9] L. Yang, Z. Li, A. Giua, Containment of rumor spread in complex social networks, Inf. Sci., 506 (2020), 113–130. https://doi.org/10.1016/j.ins.2019.07.055 doi: 10.1016/j.ins.2019.07.055
    [10] X. Liu, D. He, Nonlinear dynamic information propagation mathematic modeling and analysis based on microblog social network, Social Network Anal. Min., 10 (2020), 1–12. https://doi.org/10.1007/s13278-020-00700-4 doi: 10.1007/s13278-020-00700-4
    [11] S. Ai, S. Hong, X. Zheng, Y. Wang, X. Liu, CSRT rumor spreading model based on complex network, Int. J. Intell. Syst., 36 (2021), 1903–1913. https://doi.org/10.1002/int.22365 doi: 10.1002/int.22365
    [12] 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
    [13] A. Singh, Y. N. Singh, Rumor dynamics in weighted scale-free networks with degree correlations, J. Complex Networks, 3 (2015), 450–468. https://doi.org/10.1093/comnet/cnu047 doi: 10.1093/comnet/cnu047
    [14] F. Nian, K. Gao, Evolution of node impact based on secondary propagation, Mod. Phys. Lett. B, 34 (2020), 2050027. https://doi.org/10.1142/S021798492050027X doi: 10.1142/S021798492050027X
    [15] M. Zhang, S. Qin, X. Zhu, Information diffusion under public crisis in BA scale-free network based on SEIR model-Taking COVID-19 as an example, Physica A, 571 (2021), 125848. https://doi.org/10.1016/j.physa.2021.125848 doi: 10.1016/j.physa.2021.125848
    [16] Z, Dang, L. Li, H. Peng, J. Zhang, The behavior propagation and confrontation competition model based on information energy, Int. J. Mod. Phys. B, 35 (2021), 2150281. https://doi.org/10.1142/S0217979221502817 doi: 10.1142/S0217979221502817
    [17] H. Wang, J. M. Moore, M. Small, J. Wang, H. Yang, C. Gu, Epidemic dynamics on higher-dimensional small world networks, Appl. Math. Comput., 421 (2022), 126911. https://doi.org/10.1016/j.amc.2021.126911 doi: 10.1016/j.amc.2021.126911
    [18] K. Sznajd-Weron, J. Sznajd, Opinion evolution in closed community, Int. J. Mod. Phys. C, 11 (2000), 1157–1165. https://doi.org/10.1142/S0129183100000936 doi: 10.1142/S0129183100000936
    [19] S. Galam, Minority opinion spreading in random geometry, Eur. Phys. J. B, 25 (2002), 403–406. https://doi.org/10.1140/epjb/e20020045 doi: 10.1140/epjb/e20020045
    [20] G. Deffuant, D. Neau, F. Amblard, G. Weisbuch, Mixing beliefs among interacting agents, Adv. Complex Syst., 3 (2000), 87–98. https://doi.org/10.1142/S0219525900000078 doi: 10.1142/S0219525900000078
    [21] R. Hegselmann, U. Krause, Opinion dynamics and bounded confidence models, analysis and simulation, J. Artif. Soc. Social Simul., 5 (2002), 1–2.
    [22] H. Zhu, Y. Kong, J. Wei, J. Ma, Effect of users' opinion evolution on information diffusion in online social networks, Physica A, 492 (2018), 2034–2045. https://doi.org/10.1016/j.physa.2017.11.121 doi: 10.1016/j.physa.2017.11.121
    [23] Q. Li, Y. J. Du, Z. Y. Li, J. R. Hu, R. L. Hu, B. Y. Lv, et al., HK-SEIR model of public opinion evolution based on communication factors, Eng. Appl. Artif. Intell., 100 (2021), 104192. https://doi.org/10.1016/j.engappai.2021.104192 doi: 10.1016/j.engappai.2021.104192
    [24] D. Liang, B. Yi, Z. Xu, Opinion dynamics based on infectious disease transmission model in the non-connected context of Pythagorean fuzzy trust relationship, J. Oper. Res. Soc., 72 (2021), 2783–2803. https://doi.org/10.1080/01605682.2020.1821585 doi: 10.1080/01605682.2020.1821585
    [25] C. Wang, Dynamics of conflicting opinions considering rationality, Physica A, 560 (2020), 125160. https://doi.org/10.1016/j.physa.2020.125160 doi: 10.1016/j.physa.2020.125160
    [26] K. Mitsutsuji, S. Yamakage, The dual attitudinal dynamics of public opinion: an agent-based reformulation of LF Richardson's war-moods model, Qual. Quant., 54 (2020), 439–461. https://doi.org/10.1007/s11135-019-00938-x doi: 10.1007/s11135-019-00938-x
    [27] S. Chen, D. H. Glass, M. McCartney, Characteristics of successful opinion leaders in a bounded confidence model, Physica A, 449 (2016), 426–436. https://doi.org/10.1016/j.physa.2015.12.107 doi: 10.1016/j.physa.2015.12.107
    [28] Y. Zhao, G. Kou, Y. Peng, Y. Chen, Understanding influence power of opinion leaders in e-commerce networks: An opinion dynamics theory perspective, Inf. Sci., 426 (2018), 131–147. https://doi.org/10.1016/j.ins.2017.10.031 doi: 10.1016/j.ins.2017.10.031
    [29] Z. Feng, J. Velasco-Hernandez, B. Tapia-Santos, A mathematical model for coupling within-host and between-host dynamics in an environmentally-driven infectious disease, Math. Biosci., 241 (2013), 49–55. https://doi.org/10.1016/j.mbs.2012.09.004 doi: 10.1016/j.mbs.2012.09.004
    [30] A. Meiksin, Dynamics of COVID-19 transmission including indirect transmission mechanisms: a mathematical analysis, Epidemiol. Infect., 148 (2020), e257. https://doi.org/10.1017/S0950268820002563 doi: 10.1017/S0950268820002563
    [31] R. I. Joh, H. Wang, H. Weiss, J. S. Weitz, Dynamics of indirectly transmitted infectious diseases with immunological threshold, Bull. Math. Biol., 71 (2009), 845–862. https://doi.org/10.1007/s11538-008-9384-4 doi: 10.1007/s11538-008-9384-4
    [32] Z. Shuai, P. Van den Driessche, Global dynamics of cholera models with differential infectivity, Math. Biosci., 234 (2011), 118–126. https://doi.org/10.1016/j.mbs.2011.09.003 doi: 10.1016/j.mbs.2011.09.003
    [33] S. Liao, J. Wang, Global stability analysis of epidemiological models based on Volterra–Lyapunov stable matrices, Chaos, Solitons Fractals, 45 (2012), 966–977. https://doi.org/10.1016/j.chaos.2012.03.009 doi: 10.1016/j.chaos.2012.03.009
    [34] J. F. David, S. A. Iyaniwura, M. J. Ward, F. Brauer, A novel approach to modelling the spatial spread of airborne diseases: an epidemic model with indirect transmission, Math. Biosci. Eng., 17 (2020), 3294–3328. https://doi.org/10.3934/mbe.2020188 doi: 10.3934/mbe.2020188
    [35] F. Yin, X. Shao, J. Wu, Nearcasting forwarding behaviors and information propagation in Chinese Sina-Microblog, Math. Biosci. Eng., 16 (2019), 5380–5394. https://doi.org/10.3934/mbe.2019268 doi: 10.3934/mbe.2019268
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