Abstract
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.
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References
Global Information Assurance Certification: Information Warfare. https://www.giac.org/paper/gsec/1870/information-warfare/103284. Accessed 30 Sept 2017
Hansen, D., Shneiderman, B., Smith, M.: Analyzing Social Media Networks with NodeXL. Morgan Kaufmann, Burlington (2011). Kindle Edition
European External Action Service (EEAS): Strategic Communications Division (StratCom). https://eeas.europa.eu/headquarters/headquarters-homepage_en/100/Strategic%20Communications. Accessed 13 Dec 2017
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)
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). https://doi.org/10.4018/978-1-4666-2086-5.ch013
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). https://doi.org/10.4018/ijeoe.2013010104
Vasant, P.: Hybrid LS-SA-PS methods for solving fuzzy non-linear programming problem. Math. Comput. Model. 57(1–2), 180–188 (2013)
Vasant, P.: Hybrid simulated annealing and genetic algorithms for industrial production management problems. Int. J. Comput. Methods 7(2), 279–297 (2010)
Vasant, P., Barsoum, N.: Hybrid pattern search and simulated annealing for fuzzy production planning problems. Comput. Math Appl. 60(4), 1058–1067 (2010)
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)
Vasant, P.: Fuzzy decision making of profit function in production planning using S-curve membership function. Comput. Ind. Eng. 51(4), 715–725 (2006)
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). https://doi.org/10.4018/978-1-4666-2086-5.ch001
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)
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). https://doi.org/10.4018/978-1-4666-4450-2.ch017
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). https://doi.org/10.4018/978-1-4666-4450-2.ch032
Lavoix H.: Developing an Early Warning System for Crises, December 2008
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). https://doi.org/10.4018/jiscrm.2010100104
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). https://doi.org/10.4018/978-1-61692-865-0.ch013
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). https://doi.org/10.4018/978-1-61520-987-3.ch008
Miller, G.A.: The magical number seven - plus or minus two: some limits on our capacity for processing information. Psychol. Rev. 63(2) (1959)
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Mrsic, L., Zajec, S., Kopal, R. (2019). Appliance of Social Network Analysis and Data Visualization Techniques in Analysis of Information Propagation. In: Nguyen, N., Gaol, F., Hong, TP., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2019. Lecture Notes in Computer Science(), vol 11432. Springer, Cham. https://doi.org/10.1007/978-3-030-14802-7_11
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