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On the Social Influence in Human Behavior: Physical, Homophily, and Social Communities

  • Luca Luceri
  • Alberto Vancheri
  • Torsten Braun
  • Silvia Giordano
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 689)

Abstract

Understanding the forces governing human behavior and social dynamics is a challenging problem. Individuals’ decisions and actions are affected by interlaced factors, such as physical location, homophily, and social ties. In this paper, we propose to examine the role that distinct communities, linked to these factors, play as sources of social influence. The ego network is typically used in the social influence analysis. Our hypothesis is that individuals are embedded in communities not only related to their direct social relationships, but that involve different and complex forces. We analyze physical, homophily, and social communities to evaluate their relation with subjects’ behavior. We prove that social influence is correlated with these communities, and each one of them is (differently) significant for individuals. We define community-based features, which reflect the subject involvement in these groups, and we use them with a supervised learning algorithm to predict subject participation in social events. Results indicate that both communities and ego network are relevant sources of social influence, confirming that the ego network alone is not sufficient to explain this phenomenon. Moreover, we classify users according to the degree of social influence they experienced with respect to their groups, recognizing classes of behavioral phenotypes. To our knowledge, this is the first work that proves the existence of phenotypes related to the social influence phenomenon.

References

  1. 1.
    Anagnostopoulos, A., Kumar, R., Mahdian, M.: Influence and correlation in social networks. In: 14th ACM International Conference on Knowledge Discovery and Data Mining (2008)Google Scholar
  2. 2.
    Back, K.W.: Influence through social communication. J. Abnormal Soc. Psychol. 46(1), 9 (1951)CrossRefGoogle Scholar
  3. 3.
    Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theory Experiment (2008)Google Scholar
  4. 4.
    Crandall, D., Cosley, D., Huttenlocher, D., Kleinberg, J., Suri, S.: Feedback effects between similarity and social influence in online communities. In: 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2008)Google Scholar
  5. 5.
    Förster, A., Garg, K., Nguyen, H.A., Giordano, S.: On context awareness and social distance in human mobility traces. In: ACM Workshop on Mobile Opportunistic Networks (2012)Google Scholar
  6. 6.
    Georgiev, P., Noulas, A., Mascolo, C.: The call of the crowd: event participation in location-based social services. arXiv preprint arXiv:1403.7657 (2014)
  7. 7.
    Goyal, A., Bonchi, F., Lakshmanan, L.V.: Learning influence probabilities in social networks. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining (2010)Google Scholar
  8. 8.
    La Fond, T., Neville, J.: Randomization tests for distinguishing social influence and homophily effects. In: ACM 19th International Conference on World Wide Web (2010)Google Scholar
  9. 9.
    Liu, X., He, Q., Tian, Y., Lee, W.C., McPherson, J., Han, J.: Event-based social networks: linking the online and offline social worlds. In: ACM International Conference on Knowledge Discovery and Data Mining (2012)Google Scholar
  10. 10.
    McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: homophily in social networks. Annu. Rev. Sociol. (2001)Google Scholar
  11. 11.
    Onnela, J.P., Arbesman, S., González, M.C., Barabási, A.L., Christakis, N.A.: Geographic constraints on social network groups. PLoS one 6(4), e16939 (2011)Google Scholar
  12. 12.
    Poncela-Casasnovas, J., Gutiérrez-Roig, M., Gracia-Lázaro, C., Vicens, J., Gómez-Gardeñes, J., Perelló, J., Moreno, Y., Duch, J., Sánchez, A.: Humans display a reduced set of consistent behavioral phenotypes in dyadic games. Sci. Adv. (2016)Google Scholar
  13. 13.
    Ren, Y., Kraut, R., Kiesler, S.: Applying common identity and bond theory to design of online communities. Organ. Stud. 28(3), 377–408 (2007)Google Scholar
  14. 14.
    Saito, K., Nakano, R., Kimura, M.: Prediction of information diffusion probabilities for independent cascade model. In: Knowledge-Based Intelligent Information and Engineering SystemsGoogle Scholar
  15. 15.
    Singla, P., Richardson, M.: Yes, there is a correlation:-from social networks to personal behavior on the web. In: ACM 17th International Conference on World Wide WebGoogle Scholar
  16. 16.
    Stewart, J.Q.: An inverse distance variation for certain social influences. Science 93(2404), 89–90 (1941)CrossRefGoogle Scholar
  17. 17.
    Sunstein, C.R.: The law of group polarization. J. Polit. Philos. 10(2), 175–195 (2002)CrossRefGoogle Scholar
  18. 18.
    Tang, J., Sun, J., Wang, C., Yang, Z.: Social influence analysis in large-scale networks. In: ACM SIGKDD International Conference on Knowledge Discovery and Data MiningGoogle Scholar
  19. 19.
    Zhang, C., Shou, L., Chen, K., Chen, G., Bei, Y.: Evaluating geo-social influence in location-based social networks. In: ACM International Conference on Information and Knowledge Management (2012)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Luca Luceri
    • 1
    • 2
  • Alberto Vancheri
    • 1
  • Torsten Braun
    • 2
  • Silvia Giordano
    • 1
  1. 1.University of Applied Sciences and Arts of Southern Switzerland (SUPSI)MannoSwitzerland
  2. 2.University of BernBernSwitzerland

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