Appliance of Social Network Analysis and Data Visualization Techniques in Analysis of Information Propagation

  • Leo MrsicEmail author
  • Srecko Zajec
  • Robert Kopal
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11432)


This paper explains appliance of social network analysis and data visualization techniques in analysis of information propagation. Context of information (news) propagation through social network is an extremely dynamic and complex area to study. Due to topic actuality and a very small number of works on the similar topic this paper required a comprehensive and systematic approach. Thus, for practical reasons this work is based on the usage of Social Network Analysis (SNA) and visualization of social networking data obtained through Facebook covering 145 + public pages linked to 2.6 million fans. The main hypothesis is based on the premise whether is possible to find any similarities between the real-life social, economic and political entities/processes and online information propagation. The process consists of the development of the underlying model, the retrieval of data, data processing and consequential analysis & visualization which has been elaborated in detail along with the comments related to the methods of application.


Advanced visualization Social networks Information propagation Information warfare SNA (Social Network Analysis) Facebook 


  1. 1.
    Global Information Assurance Certification: Information Warfare. Accessed 30 Sept 2017
  2. 2.
    Hansen, D., Shneiderman, B., Smith, M.: Analyzing Social Media Networks with NodeXL. Morgan Kaufmann, Burlington (2011). Kindle EditionGoogle Scholar
  3. 3.
    European External Action Service (EEAS): Strategic Communications Division (StratCom). Accessed 13 Dec 2017
  4. 4.
    Phruksaphanrat, B.: Preemptive possibilistic linear programming: application to aggregate production planning. In: Proceedings of World Academy of Science, Engineering and Technology, vol. 80, pp. 473–480 (2011)Google Scholar
  5. 5.
    Purnomo, H.D., Wee, H.: Soccer game optimization: an innovative integration of evolutionary algorithm and swarm intelligence algorithm. In: Vasant, P. (ed.) Meta-Heuristics Optimization Algorithms in Engineering, Business, Economics, and Finance, pp. 386–420. Information Science Reference, Hershey (2013).
  6. 6.
    Sadeghi, M., Hosseini, H.M.: Evaluation of fuzzy linear programming application in energy models. Int. J. Energy Optim. Eng. (IJEOE) 2(1), 50–59 (2013). Scholar
  7. 7.
    Vasant, P.: Hybrid LS-SA-PS methods for solving fuzzy non-linear programming problem. Math. Comput. Model. 57(1–2), 180–188 (2013)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Vasant, P.: Hybrid simulated annealing and genetic algorithms for industrial production management problems. Int. J. Comput. Methods 7(2), 279–297 (2010)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Vasant, P., Barsoum, N.: Hybrid pattern search and simulated annealing for fuzzy production planning problems. Comput. Math Appl. 60(4), 1058–1067 (2010)CrossRefGoogle Scholar
  10. 10.
    Vasant, P., Ganesan, T., Elamvazuthi, I., Webb, J.F.: Fuzzy linear programming for the production planning: the case of Textile Firm. Int. Rev. Model. Simul. 4(2), 961–970 (2011)Google Scholar
  11. 11.
    Vasant, P.: Fuzzy decision making of profit function in production planning using S-curve membership function. Comput. Ind. Eng. 51(4), 715–725 (2006)CrossRefGoogle Scholar
  12. 12.
    Vo, D.N., Schegner, P.: An improved particle swarm optimization for optimal power flow. In: Vasant, P. (ed.) Meta-Heuristics Optimization Algorithms in Engineering, Business, Economics, and Finance, pp. 1–40. Information Science Reference, Hershey (2013).
  13. 13.
    Xiao, Z., Xia, S., Gong, K., Li, D.: The trapezoidal fuzzy soft set and its application in MCDM. Appl. Math. Model. 36(12), 5844–5855 (2012)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Klepac, G.: Data mining models as a tool for churn reduction and custom product development in telecommunication industries. In: Vasant, P. (ed.) Handbook of Research on Novel Soft Computing Intelligent Algorithms: Theory and Practical Applications, pp. 511–537. Information Science Reference, Hershey (2014).
  15. 15.
    Mršić, L.: Widely applicable multi-variate decision support model for market trend analysis and prediction with case study in retail. Vasant, P. (ed.) Handbook of Research on Novel Soft Computing Intelligent Algorithms: Theory and Practical Applications, pp. 989–1018. Information Science Reference, Hershey (2014).
  16. 16.
    Lavoix H.: Developing an Early Warning System for Crises, December 2008Google Scholar
  17. 17.
    Li, L., Goodchild, M.F.: The role of social networks in emergency management: a research agenda. Int. J. Inf. Syst. Crisis Response Manage. (IJISCRAM) 2(4), 48–58 (2010). Scholar
  18. 18.
    Lombardo, R.: Data mining and explorative multivariate data analysis for customer satisfaction study. In: Koyuncugil, A., Ozgulbas, N. (eds.) Surveillance Technologies and Early Warning Systems: Data Mining Applications for Risk Detection, pp. 243–266. Information Science Reference, Hershey (2011). Scholar
  19. 19.
    Meissen, U., Voisard, A.: Current state and solutions for future challenges in early warning systems and alerting technologies. In: Asimakopoulou, E., Bessis, N. (eds.) Advanced ICTs for Disaster Management and Threat Detection: Collaborative and Distributed Frameworks, pp. 108–130. Information Science Reference, Hershey (2010).
  20. 20.
    Miller, G.A.: The magical number seven - plus or minus two: some limits on our capacity for processing information. Psychol. Rev. 63(2) (1959)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Algebra University CollegeZagrebCroatia

Personalised recommendations