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Social Relevance Index for Studying Communities in a Facebook Group of Patients

  • Laura Sani
  • Gianfranco Lombardo
  • Riccardo Pecori
  • Paolo Fornacciari
  • Monica Mordonini
  • Stefano Cagnoni
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10784)

Abstract

Identifying Relevant Sets, i.e., variable subsets that exhibit a coordinated behavior, in complex systems is a very relevant research topic. Systems that exhibit complex dynamics are, for example, social networks, which are characterized by complex and dynamic relationships among users. A challenging topic within this context regards the identification of communities or subsets of users, both within the whole network and within specific groups. We applied the Relevance Index method, which has been shown to be effective in many situations, to the study of communities of users in the Facebook group of the Italian association of patients affected by Hidradenitis Suppurativa. Since the need for computing the Relevance Index for each possible variable subset of users makes the exhaustive computation unfeasible, we resorted to the help of an efficient niching evolutionary metaheuristic, hybridized with local searches. The communities detected through the aforementioned method have been studied to search similarities in terms of number of posts, sentiments, number of contacts, roles, behaviors, etc. The results demonstrate that it is possible to detect such subsets of users in the particular Facebook group we analyzed.

Keywords

Complex systems Relevant sets Social network Community detection Evolutionary metaheuristic 

References

  1. 1.
    Prokopenko, M., Boschetti, F., Ryan, A.J.: An information-theoretic primer on complexity, self-organization, and emergence. Complexity 15(1), 11–28 (2009)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Villani, M., Filisetti, A., Benedettini, S., Roli, A., Lane, D., Serra, R.: The detection of intermediate-level emergent structures and patterns. In: Miglino, O., et al. (eds.) Advances in Artificial Life, ECAL 2013, pp. 372–378. The MIT Press (2013). http://mitpress.mit.edu/books/advances-artificial-life-ecal-2013
  3. 3.
    Pecori, R.: A comparison analysis of trust-adaptive approaches to deliver signed public keys in P2P systems. In: 2015 7th International Conference on New Technologies, Mobility and Security (NTMS), pp. 1–5, July 2015Google Scholar
  4. 4.
    Pecori, R., Veltri, L.: 3AKEP: triple-authenticated key exchange protocol for peer-to-peer VoIP applications. Comput. Commun. 85, 28–40 (2016)CrossRefGoogle Scholar
  5. 5.
    Canale, S., Giorgio, A.D., Lisi, F., Panfili, M., Celsi, L.R., Suraci, V., Priscoli, F.D.: A future internet oriented user centric extended intelligent transportation system. In: 2016 24th Mediterranean Conference on Control and Automation (MED), pp. 1133–1139, June 2016Google Scholar
  6. 6.
    Fornacciari, P., Mordonini, M., Tomaiuolo, M.: Social network and sentiment analysis on twitter: towards a combined approach. In: KDWeb (2015)Google Scholar
  7. 7.
    Sani, L., et al.: Efficient search of relevant structures in complex systems. In: Adorni, G., Cagnoni, S., Gori, M., Maratea, M. (eds.) AI*IA 2016. LNCS (LNAI), vol. 10037, pp. 35–48. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-49130-1_4 CrossRefGoogle Scholar
  8. 8.
    Gershenson, C., Fernandez, N.: Complexity and information: measuring emergence, self-organization, and homeostasis at multiple scales. Complex. 18(2), 29–44 (2012)CrossRefGoogle Scholar
  9. 9.
    Prokopenko, M., Lizier, J.T., Obst, O., Wang, X.R.: Relating fisher information to order parameters. Phys. Rev. E 84, 041116 (2011). https://link.aps.org/doi/10.1103/PhysRevE.84.041116 CrossRefGoogle Scholar
  10. 10.
    Zubillaga, D., Cruz, G., Aguilar, L.D., Zapotécatl, J., Fernández, N., Aguilar, J., Rosenblueth, D.A., Gershenson, C.: Measuring the complexity of self-organizing traffic lights. Entropy 16(5), 2384–2407 (2014). http://www.mdpi.com/1099-4300/16/5/2384 CrossRefGoogle Scholar
  11. 11.
    Villani, M., Roli, A., Filisetti, A., Fiorucci, M., Poli, I., Serra, R.: The search for candidate relevant subsets of variables in complex systems. Artif. Life 21(4), 412–431 (2015)CrossRefGoogle Scholar
  12. 12.
    Tononi, G., Sporns, O., Edelman, G.M.: A measure for brain complexity: relating functional segregation and integration in the nervous system. Proc. Natl. Acad. Sci. 91(11), 5033–5037 (1994)CrossRefGoogle Scholar
  13. 13.
    Tononi, G., McIntosh, A., Russel, D., Edelman, G.: Functional clustering: identifying strongly interactive brain regions in neuroimaging data. Neuroimage 7, 133–149 (1998)CrossRefGoogle Scholar
  14. 14.
    Filisetti, A., Villani, M., Roli, A., Fiorucci, M., Poli, I., Serra, R.: On some properties of information theoretical measures for the study of complex systems. In: Pizzuti, C., Spezzano, G. (eds.) WIVACE 2014. CCIS, vol. 445, pp. 140–150. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-12745-3_12 Google Scholar
  15. 15.
    Scott, J.: Social Network Analysis. Sage Publications (2017)Google Scholar
  16. 16.
    Tasgin, M., Herdagdelen, A., Bingol, H.: Community detection in complex networks using genetic algorithms. arXiv preprint arXiv:0711.0491 (2007)
  17. 17.
    Pizzuti, C.: GA-Net: a genetic algorithm for community detection in social networks. In: Rudolph, G., Jansen, T., Beume, N., Lucas, S., Poloni, C. (eds.) PPSN 2008. LNCS, vol. 5199, pp. 1081–1090. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-87700-4_107 CrossRefGoogle Scholar
  18. 18.
    Li, J., Song, Y.: Community detection in complex networks using extended compact genetic algorithm. Soft Comput. 17(6), 925–937 (2013)CrossRefGoogle Scholar
  19. 19.
    Guerrero, M., Montoya, F.G., Baos, R., Alcayde, A., Gil, C.: Adaptive community detection in complex networks using genetic algorithms. Neurocomputing 266(Suppl. C), 101–113 (2017)CrossRefGoogle Scholar
  20. 20.
    Bucur, D., Iacca, G., Marcelli, A., Squillero, G., Tonda, A.: Multi-objective evolutionary algorithms for influence maximization in social networks. In: Squillero, G., Sim, K. (eds.) EvoApplications 2017. LNCS, vol. 10199, pp. 221–233. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-55849-3_15 CrossRefGoogle Scholar
  21. 21.
    Cover, T., Thomas, A.: Elements of Information Theory, 2nd edn. Wiley-Interscience, New York (2006)zbMATHGoogle Scholar
  22. 22.
    Vicari, E., et al.: GPU-based parallel search of relevant variable sets in complex systems. In: Rossi, F., Piotto, S., Concilio, S. (eds.) WIVACE 2016. CCIS, vol. 708, pp. 14–25. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-57711-1_2 CrossRefGoogle Scholar
  23. 23.
    Filisetti, A., Villani, M., Roli, A., Fiorucci, M., Serra, R.: Exploring the organisation of complex systems through the dynamical interactions among their relevant subsets. In: Andrews, P. et al. (ed.) Proceedings of the European Conference on Artificial Life 2015, ECAL 2015, pp. 286–293. The MIT Press (2015)Google Scholar
  24. 24.
    Lombardo, G., Ferrari, A., Fornacciari, P., Mordonini, M., Sani, L., Tomaiuolo, M.: Dynamics of emotions and relations in a facebook group of patients with hidradenitis suppurativa. In: Guidi, B., Ricci, L., Calafate, C.T., Gaggi, O., Marquez-Barja, J. (eds.) GOODTECHS 2017. LNICST, vol. 233, pp. 269–278. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-76111-4_27 CrossRefGoogle Scholar
  25. 25.
    Angiani, G., Cagnoni, S., Chuzhikova, N., Fornacciari, P., Mordonini, M., Tomaiuolo, M.: Flat and hierarchical classifiers for detecting emotion in tweets. In: Adorni, G., Cagnoni, S., Gori, M., Maratea, M. (eds.) AI*IA 2016. LNCS (LNAI), vol. 10037, pp. 51–64. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-49130-1_5 CrossRefGoogle Scholar
  26. 26.
    Parrott, W.G.: Emotions in Social Psychology: Essential Readings. Psychology Press, New York (2001)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Dip. di Ingegneria e ArchitetturaUniversità di ParmaParmaItaly
  2. 2.SMARTEST Research CentreUniversità eCAMPUSNovedrateItaly

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