A model of an online social network (OSN)

Eric R. Kaufmann , Joie Lea Murorunkwere

Electronic Research Archive ›› 2026, Vol. 34 ›› Issue (9) : 6448 -6462.

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Electronic Research Archive ›› 2026, Vol. 34 ›› Issue (9) :6448 -6462. DOI: 10.3934/era.2026282
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A model of an online social network (OSN)
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Abstract

We introduce a new epidemic framework to model the adoption and abandonment dynamics of users on an online social network (OSN), with special emphasis on the novel compartment pauci-engaged. Pauci-engaged individuals have been exposed to an invitation to an OSN, but remain undecided about joining, thus reflecting real-world hesitation and trial behaviors. Building on the infectious–recovery dynamics studied by Chen, Kong, and Wang, we derive the basic reproduction number R0 and establish threshold conditions for the stability of both the user-free equilibrium and the user-prevailing equilibrium. Local stability analysis using the Jacobian matrix, together with global stability results based on Lyapunov functions, shows that when the basic reproduction number R0 < 1, the OSN cannot sustain an engaged user base, while when R0 > 1, a persistent engagement state emerges. Our findings highlight the pivotal role of the decision-making phase in the growth of the digital platform and offer actionable insights to optimize user recruitment and retention strategies in OSNs.

Keywords

online social network / equilibrium / stability analysis

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Eric R. Kaufmann, Joie Lea Murorunkwere. A model of an online social network (OSN). Electronic Research Archive, 2026, 34 (9) : 6448-6462 DOI:10.3934/era.2026282

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References

[1]

M. Bailey, R. Cao, T. Kuchler, J. Stroebel, The economic effects of social networks: Evidence from the housing market, J. Polit. Econ., 126 (2018), 2224-2276. https://doi.org/10.1086/700073

[2]

D. M. Boyd, N. B. Ellison, Social networks sites: Definition, history and scholarship, J. Comput.-Mediated Commun., 13 (2007), 210-230. https://doi.org/10.1111/j.1083-6101.2007.00393.x

[3]

F. Griffiths, T. Dobermann, J. A. K. Cave, M. Thorogood, S. Johnson, K. Salamatian, et al., The impact of online social networks on health and health systems: A scoping review and case studies, Policy Internet, 7 (2015), 473-496. https://doi.org/10.1002/poi3.97

[4]

R. M. Anderson, R. M. May, Infectious Diseases of Humans: Dynamics and Control , Oxford University Press, Oxford, 1991. https://doi.org/10.1017/S0950268800059896

[5]

L. M. Bettencourt, A. Cintrón-Arias, D. I. Kaiser, C. Castillo-Chávez, The power of a good idea: Quantitative modeling of the spread of ideas from epidemiological models, Phys. A, 364 (2006), 513-536. https://doi.org/10.1016/j.physa.2005.08.083

[6]

M. E. J. Newman, The structure and function of complex networks, SIAM Rev., 45 (2003), 167-256. https://doi.org/10.1137/S003614450342480

[7]

R. Chen, L. Kong, M. Wang, Modeling the dynamics of adoption and abandonment of multiple products, Math. Comput. Simul., 241 (2026), 868-889. https://doi.org/10.1016/j.matcom.2025.10.026

[8]

S. Wu, A. D. Sarma, A. Fabrikant, S. Lattanzi, A. Tomkins, Arrival and departure dynamics in social networks, in Proceedings of the Sixth ACM International Conference on Web Search and Data Mining , (2013), 233-242. https://doi.org/10.1145/2433396.2433425

[9]

B. Du, H. Wang, M. Liu, An information diffusion model in social networks with carrier compartment and delay, Nonlinear Anal. Model. Control, 23 (2018), 568-582. https://doi.org/10.15388/NA.2018.4.7

[10]

T. Wang, J. He, X. Wang, An information spreading model based on online social networks, Phys. A, 490 (2018), 488-496. https://doi.org/10.1016/j.physa.2017.08.078

[11]

R. Pastor-Satorras, A. Vespignani, Epidemic spreading in scale-free networks, Phys. Rev. Lett., 86 (2001), 3200-3203. https://doi.org/10.1103/PhysRevLett.86.3200

[12]

N. Kimmel, L. Kong, M. Wang, Modeling the dynamics of user adoption and abandonment in online social networks, Math. Methods Appl. Sci., 48 (2025), 1853-1868. https://doi.org/10.1002/mma.10413

[13]

C. Lei, Z. Lin, H. Wang, The free boundary problem describing information diffusion in online social networks, J. Differ. Equations, 254 (2013), 1326-1341. http://dx.doi.org/10.1016/j.jde.2012.10.021

[14]

J. R. Graef, L. Kong, A. Ledoan, M. Wang, Stability analysis of a fractional online social network model, Math. Comput. Simul., 178 (2020), 625-645. https://doi.org/10.1016/j.matcom.2020.07.012

[15]

R. Chen, L. Kong, M. Wang, Stability analysis of an online social network model, Rocky Mt. J. Math., 53 (2023), 1019-1041. https://doi.org/10.1216/rmj.2023.53.1019

[16]

Z. K. Zhang, C. Liu, X. X. Zhan, X. Lu, C. X. Zhang, Y. C. Zhang, Dynamics of information diffusion and its applications on complex networks, Phys. Rep., 651 (2016), 1-34. https://doi.org/10.1016/j.physrep.2016.07.002

[17]

G. Q. Sun, R. He, L. F. Hou, X. Luo, S. Gao, L. Chang, et al., Optimal control of spatial diseases spreading in networked reaction-diffusion systems, Phys. Rep., 1111 (2025), 1-64. https://doi.org/10.1016/j.physrep.2025.01.005

[18]

M. Martcheva, An Introduction to Mathematical Epidemiology , Springer, New York, 61 (2015), 9-31.

[19]

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

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