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Modelling and Simulating a Opinion Diffusion on Twitter Using a Multi-agent Simulation of Virus Propagation

  • Carlos Rodríguez Lucatero
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10633)

Abstract

Nowadays Twitter is one of most popular social communication network on the web. Because of that it is very important to try to understand the phenomena of propagation of opinions that take place in these media. A theoretical tool that allows the analysis of phenomena in networks is the theory of graphs. So if we model a social network by means of a graph we can make use of the solid theoretical concepts of graph theory to try to explain phenomena such as the polarization of opinions in social networks. Graph theory concepts as for instance centrality can be used to identify users, modelled as nodes of a graph, that have more influence or popularity in a social network. That can be used to classify users. Another useful graph parameter is the connectedness that enable to know how robust is a given network topology. In this article we will be interested in studying the impact of the topological structure of a network as well as the conditions of probability of contagion and recovery of the nodes that allow the polarization of an opinion or on the contrary the extinction of this one. Given that the calculation of exact values of the fast extinction threshold are hard to obtain even for very simple versions of the problem, it is a good idea to apply simulation to get a clue about the parameter values of the system that make the information survive or get extincted in a network. For this end, based on a virus propagation mathematical model, we are going to simulate the opinion propagation using a multi-agent testbed known as Netlogo and implement a opinion propagation mathematical model under a dynamical system approach using the MATLAB programming language.

References

  1. 1.
    Leskovec, J., Chakrabarti, D., Faloutsos, C., Madden, S., Guestrin, C., Faloutsos, M.: Information survival threshold in sensor and P2P networks. In: IEEE INFOCOM 2007 (2007)Google Scholar
  2. 2.
    Kempe, D., Kleinberg., J.: Protocols and impossibility results for gossip-based communication mechanisms. In: Proceedings of the Symposium on Foundations of Computer Science, FOCS 2002 (2002)Google Scholar
  3. 3.
    Borgs, C., Chayes, J., Ganesh, A., Saberi, A.: How to distribute antidote to control epidemics. Random Struct. Algorithms 37, 204–222 (2010)MathSciNetzbMATHGoogle Scholar
  4. 4.
    Durrett, R., Liu, X.F.: The contact process on a finite set. Ann. Probab. 16(3), 1158–1173 (1988)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Albert, R., Barabási, A.: Error and attack tolerance of complex networks. Nature 406, 378 (2000)CrossRefGoogle Scholar
  6. 6.
    Barabási, A., Albert, R.: Emergence of scaling in random graphs. Science 286, 509–512 (1999)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Schwartz, N., Cohen, R., Ben-Avraham, D., Barabási, A.L., Havlin, S.: Percolation in directed scale-free networks. Phys. Rev. E 66(1), 0151041–0151044 (2002)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Alon, N., Benjamini, I., Stacey, A.: Percolation on finite graphs and isoperimetric inequalities. Ann. Probab. 32, 1727–1745 (2004)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Balister, P., Bollobás, B.: Bond percolation with attenuation in high dimensional Voronoi tilings. Random Struct. Algorithms 36, 5–10 (2009)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Faloutsos, M., Faloutsos, P., Faloutsos, C.: On power-law relationships of the internet topology. In: Proceedings SIGCOMM 1999 (1999)Google Scholar
  11. 11.
    Radicchi, F., Ramasco, J.J., Barrat, A., Fortunato, S.: Complex networks renormalization: flows and fixed points. Phys. Rev. Lett. 101, 1487011–1487014 (2008)CrossRefGoogle Scholar
  12. 12.
    Gonzalez-Avella, J., Cosenza, M., Tucci, K.: Nonequilibrium transition induced by mass media in a model for social influence. Phys. Rev. E 72, 0651021–0651024 (2005)CrossRefGoogle Scholar
  13. 13.
    Nicosia, V., Bagnoli, F., Latora, V.: Impact of network structure on a model of diffusion and competitive interaction. Phys. Rev. E 67, 0261201–0261206 (2003)Google Scholar
  14. 14.
    Riquelme, F.: Measuring user influence on Twitter: a survey. arXiv:cs.SI/1508.07951v1, pp. 1–24 (2015)
  15. 15.
    Pastor-Satorras, R., Vespignani, A.: Epidemic dynamics and endemic states in complex networks. Phys. Rev. E 63, 0661171–0661178 (2001)CrossRefGoogle Scholar
  16. 16.
    Pastor-Satorras, R., Vespignani, A.: Epidemic spreading in scale-free networks. Phys. Rev. Lett. 86, 3200–3203 (2001)CrossRefGoogle Scholar
  17. 17.
    Pastor-Satorras, R., Vespignani, A.: Epidemic dynamics in finite size scale-free networks. Phys. Rev. E 65, 0351081–0351084 (2002)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Information Technologies DepartmentUniversidad Autónoma Metropolitana Unidad CuajimalpaMexico CityMexico

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