Skip to main content
SpringerLink
Log in

Modeling and intelligent optimization of social collective behavior with online public opinion synchronization

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

Today, the public opinion synchronization on the network platform is becoming one of important issues worthy of careful study. In this paper, we take synchronization evolution phenomenon as an objective and adopt artificial bee colony (ABC) to evaluate network synchronization effects with optimization theory to find out an appropriate network structure. Firstly, we use the Kuramoto oscillators as a metaphor of the social system collective behavior. Secondly, combined with the social network characteristics obtained from the data of Sina Micro-Blog, a synchronization evolution model of Internet public opinion based on Kuramoto one is established. Subsequently, evolutionary multi-objective optimization model is set up and the ABC method is used to optimize the level of network synchronization, the synchronization starting time and the cost of public opinion synchronization. Finally, case analysis on “Double Eleven” Internet Marketing as well as Cadmium Poisoned Rice Event demonstrates that the synchronization performance of weak coupled system can be enhanced by offering reasonable configuration of the connection cost and the synchronization duration cost. In addition, a certain degree of increase in input cost can promote the synchronization performance and extend the synchronization duration significantly.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Xiao 1R, Hou J, Li J (2017) Dynamic evolution of government’s public trust in online collective behaviour: a social computing approach. Int J Bio Inspir Comput 9(1):1–18

    Article  Google Scholar 

  2. Hong 2H, Kim BJ, Choi MY, Park H (2004) Factors that predict better synchronizability on complex networks. Phys Rev E 69(2):067105

    Article  Google Scholar 

  3. Esfahani 3RK, Shahbazi F, Samani KA (2012) Noise-induced synchronization in small world networks of phase oscillators. Phys Rev E 86(3):036204

    Article  Google Scholar 

  4. Yaghoobi 4T, Esmaeili E (2017) An improved artificial bee colony algorithm for global numerical optimisation. Int J Bio Inspir Comput 9(4):251–258

    Article  Google Scholar 

  5. Wang 5J, Wang YQ (2015) SIR rumor spreading model with network medium in complex social networks. Chin J Phys 53(1):1–21

    MathSciNet  Google Scholar 

  6. Woo 6J, Chen H (2016) Epidemic model for information diffusion in web forums: experiments in marketing exchange and political dialog. Springerplus 5(1):66

    Article  Google Scholar 

  7. Pikovsky 7A, Rosenblum M, Kurths J, Synchronization: a universal concept in nonlinear sciences. Cambridge University Press, Cambridge 2001

    Book  MATH  Google Scholar 

  8. Winfree 8AT (1967) Biological rhythms and the behavior of populations of coupled oscillators. J Theor Biol 16(1):15–42

    Article  Google Scholar 

  9. John 9B, Buck E (1978) Toward a functional interpretation of synchronous flashing by fireflies. Am Nat 112(985):471–492

    Article  Google Scholar 

  10. Neda 10Z, Ravasz E, Vicsek T et al (2000) The sound of many hands clapping. Nature 403:849–850

    Article  Google Scholar 

  11. Li DY, Liu K, Sun Y, Han MC (2008) Emergent computation: virtual reality from disordered clapping to ordered clapping. Sci China F Inf Sci 51(5):449–459

    Article  MATH  Google Scholar 

  12. Strogatz 12SH (2000) From Kuramoto to Crawford: exploring the onset of synchronization in populations of coupled oscillators. Physica D 143(1–4):1–20

    Article  MathSciNet  MATH  Google Scholar 

  13. Nakao 13M, Yamamoto K, Katayama N, Yamamoto M (2002) Phase shift of coupled oscillator model with feedbacks in response to multiple bright light exposure. Psychiatry Clin Neurosci 56(3):215–216

    Article  Google Scholar 

  14. Stauffer 14D (2003) Sociophysics simulations. Comput Sci Eng 5(3):71–75

    Article  MATH  Google Scholar 

  15. Pluchino 15A, Latora V, Rapisarda A (2005) Changing opinions in a changing world: a new perspective in sociophysics. Int J Mod Phys C 16(4):515–531

    Article  MATH  Google Scholar 

  16. Long 16Q (2017) A framework for data-driven computational experiments of inter-organizational collaborations in supply chain networks. Inf Sci 399:43–63

    Article  Google Scholar 

  17. Babiceanu 17RF, Large-scale computational experiments for complex enterprise systems behaviour prediction. IFAC Proceedings, 45(6): 691–696, 2012

    Google Scholar 

  18. Xue 18X, Wang S, Bin G, Hou Z (2016) A computational experiment-based evaluation method for context-aware services in complicated environment. Inf Sci 373:269–286

    Article  Google Scholar 

  19. Surender 19S, Reddy BK, Panigrahi (2017) Application of swarm intelligent techniques with mixed variables to solve optimal power flow problems. Int J Bio Inspir Comput 10(4):283–292

    Article  Google Scholar 

  20. Taormina 20R, Chau KW (2015) Data-driven input variable selection for rainfall-runoff modeling using binary-coded particle swarm optimization and extreme learning machines. J Hydrol 529(3):1617–1632

    Article  Google Scholar 

  21. Zhang 21SW, Chau KW (2009) Dimension reduction using semi-supervised locally linear embedding for plant leaf classification. Lect Notes Comput Sci 5754:948–955

    Article  Google Scholar 

  22. Khan 22A, Niemann-Delius C (2014) Production scheduling of open pit mines using particle swarm optimization algorithm. Adv Oper Res 2014:9 (Article ID 208502)

    MathSciNet  Google Scholar 

  23. Wu 23CL, Chau KW, Li YS (2009) Methods to improve neural network performance in daily flows prediction. J Hydrol 372(1–4):80–93

    Article  Google Scholar 

  24. Sultan 24JA, Hasan AL (2016) Solving fuzzy multi-objective master production scheduling problems using hybrid particle swarm optimization: an industrial case study. Int J Math Comput Sci 3(5):1–9

    Google Scholar 

  25. Basturk 25B, Asturk B, Karaboga D (2007) Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems. In: Proceedings of the 12th international fuzzy systems association world congress on foundations of fuzzy logic and soft computing. pp 789–798

  26. Wong 26L, Malcolm YHL, Chin SC (2007) A bee colony optimization algorithm to schedule the job shop. Nanyang Technological University Press, Singapore

    Google Scholar 

  27. Wong 27L, Malcolm YHL, Chin SC (2008) Bee colony optimization with local search for traveling salesman problem. Nanyang Technological University Press, Singapore

    Google Scholar 

  28. Singh 28A (2009) An artificial bee colony algorithm for the leaf constrained minimum spanning tree problem. Appl Soft Comput 9(2):625–631

    Article  Google Scholar 

  29. Chen 29T, Xiao R (2013) A dynamic intelligent decision approach to dependency modeling of project tasks in complex engineering system optimization. Math Problems Eng 2013:15 (Article ID 398123)

    MathSciNet  MATH  Google Scholar 

  30. Chen 30T, Xiao R (2014) Modeling design iteration in product design and development and its solution by a novel artificial bee colony algorithm. Comput Intell Neurosci 2014:13 (Article ID 240828)

    Article  Google Scholar 

  31. Liu QN (2013) Microblog data mining based on complex network. Beijing University of Posts and Telecommunications Press, Beijing. (In Chinese)

    Google Scholar 

  32. Goh 32KI, Kahng B, Kim D (2001) Universal behavior of load distribution in scale-free networks. Phys Rev Lett 87(27):278701

    Article  Google Scholar 

  33. Karaboga 33D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214(1):108–132

    MathSciNet  MATH  Google Scholar 

  34. Xiao 34R, Wang Y, Tao Z (2014) Research on structure emergence based on cellular automata. Int J Bio Inspir Comput 6(2):126–139

    Article  Google Scholar 

  35. Xiao 35R, Zhang Y, Huang Z (2015) Emergent computation of complex systems: a comprehensive review. Int J Bio Inspir Comput 7(2):75–97

    Article  Google Scholar 

  36. Karafyllidis 36I (1999) Acceleration of cellular automata algorithms using genetic algorithms. Adv Eng Softw 30(6):419–437

    Article  Google Scholar 

  37. Sidiropoulos 37E, Tolikas P (2008) Genetic algorithms and cellular automata in aquifer management. Appl Math Model 32(4):617–640

    Article  MathSciNet  MATH  Google Scholar 

  38. Yang 38X, Zheng XQ, Lv LN (2012) A spatiotemporal model of land use change based on ant colony optimization, Markov chain and cellular automata. Ecol Model 233:11–19

    Article  Google Scholar 

  39. Naghibi 39F, Delavar MR, Pijanowski B (2016) Urban growth modeling using cellular automata with multi-temporal remote sensing images calibrated by the artificial bee colony optimization algorithm. Sensors 16(12):2122

    Article  Google Scholar 

  40. Shi 40Y, Liu HC, Gao L, Zhang GH (2011) Cellular particle swarm optimization. Inf Sci 181(20):4460–4493

    Article  MathSciNet  MATH  Google Scholar 

  41. Gholizadeh 41S (2013) Layout optimization of truss structures by hybridizing cellular automata and particle swarm optimization. Comp Struct 125:86–99

    Article  Google Scholar 

  42. Zhang 42Y, Xiao R (2014) Synchronization of Kuramoto oscillators in small-world networks. Phys A 416:33–40

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

This research is supported by National Natural Science Foundation of China (No. 71401156 and 61540032), Zhejiang Provincial Natural Science Foundation of China (No. LY18G010001) as well as Contemporary Business and Trade Research Center and Center for Collaborative Innovation Studies of Modern Business of Zhejiang Gongshang University of China (Grant No. 14SMXY05YB).

Author information

Authors and Affiliations

  1. School of Automation, Huazhong University of Science and Technology, Wuhan, 430074, People’s Republic of China

    Renbin Xiao & Jin Li

  2. Hangzhou College of Commerce, Zhejiang Gongshang University, Hangzhou, 310018, People’s Republic of China

    Tinggui Chen

  3. School of Management and E-Business, Zhejiang Gongshang University, Hangzhou, 310018, People’s Republic of China

    Tinggui Chen

Authors
  1. Renbin Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tinggui Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tinggui Chen.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xiao, R., Li, J. & Chen, T. Modeling and intelligent optimization of social collective behavior with online public opinion synchronization. Int. J. Mach. Learn. & Cyber. 10, 1979–1996 (2019). https://doi.org/10.1007/s13042-018-0854-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-018-0854-1

Keywords

Search

Navigation