Social Influence: From Contagion to a Richer Causal Understanding

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10047)


A central problem in the analysis of observational data is inferring causal relationships - what are the underlying causes of the observed behaviors? With the recent proliferation of Big Data from online social networks, it has become important to determine to what extent social influence causes certain messages to ‘go viral’, and to what extent other causes also play a role. In this paper, we present a causal framework showing that social influence is confounded with personal similarity, traits of the focal item, and external circumstances. Combined with a set of qualitative considerations on the combination of these sources of causation, we show how this framework can enable investigators to systematically evaluate, strengthen and qualify causal claims about social influence, and we demonstrate its usefulness and versatility by applying it to a variety of common online social datasets.


Social influence Contagion Causal inference Graphical causal models Confounding Computational social science 


  1. 1.
    Ackland, R.: Web social science: Concepts, data and tools for social scientists in the digital age. Sage, London (2013)Google Scholar
  2. 2.
    Alshamsi, A., Pianesi, F., Lepri, B., Pentland, A., Rahwan, I.: Beyond contagion: Reality mining reveals complex patterns of social influence. PloS One 10(8), e0135740 (2015)CrossRefGoogle Scholar
  3. 3.
    Anagnostopoulos, A., Kumar, R., Mahdian, M.: Influence and correlation in social networks. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 7–15. ACM (2008)Google Scholar
  4. 4.
    Aral, S., Muchnik, L., Sundararajan, A.: Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. Proc. Nat. Acad. Sci. 106(51), 21544–21549 (2009)CrossRefGoogle Scholar
  5. 5.
    Aral, S., Walker, D.: Identifying influential and susceptible members of social networks. Science 337(6092), 337–341 (2012)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Bakshy, E., Hofman, J.M., Mason, W.A., Watts, D.J.: Everyone’s an influencer: quantifying influence on twitter. In: Proceedings of the fourth ACM international conference on Web search and data mining. pp. 65–74. ACM (2011)Google Scholar
  7. 7.
    Bakshy, E., Rosenn, I., Marlow, C., Adamic, L.: The role of social networks in information diffusion. In: Proceedings of the 21st International Conference on World Wide Web, pp. 519–528. ACM (2012)Google Scholar
  8. 8.
    Barbieri, N., Bonchi, F., Manco, G.: Influence-based network-oblivious community detection. In: 2013 IEEE 13th International Conference on Data Mining (ICDM), pp. 955–960. IEEE (2013)Google Scholar
  9. 9.
    Barnett, L., Barrett, A.B., Seth, A.K.: Granger causality and transfer entropy are equivalent for gaussian variables. Phys. Rev. Lett. 103(23), 238701 (2009)CrossRefGoogle Scholar
  10. 10.
    Berger, J.: Contagious: Why Things catch on. Simon and Schuster, New York (2013)Google Scholar
  11. 11.
    Borge-Holthoefer, J., Perra, N., Gonçalves, B., González-Bailón, S., Arenas, A., Moreno, Y., Vespignani, A.: The dynamics of information-driven coordination phenomena: A transfer entropy analysis. Sci. Adv. 2(4), e1501158 (2016)CrossRefGoogle Scholar
  12. 12.
    Cebrian, M., Rahwan, I., Pentland, A.S.: Beyond viral. Commun. ACM 59(4), 36–39 (2016)CrossRefGoogle Scholar
  13. 13.
    Centola, D., Macy, M.: Complex contagions and the weakness of long ties1. Am. J. Soc. 113(3), 702–734 (2007)CrossRefGoogle Scholar
  14. 14.
    Cha, M., Haddadi, H., Benevenuto, F., Gummadi, P.K.: Measuring user influence in twitter: The million follower fallacy. ICWSM 10, 10–17 (2010)Google Scholar
  15. 15.
    Cheng, J., Adamic, L., Dow, P.A., Kleinberg, J.M., Leskovec, J.: Can cascades be predicted? In: Proceedings of the 23rd International Conference on World Wide Web, pp. 925–936. International World Wide Web Conferences Steering Committee (2014)Google Scholar
  16. 16.
    Chikhaoui, B., Chiazzaro, M., Wang, S.: A new granger causal model for influence evolution in dynamic social networks: The case of dblp. In: Twenty-Ninth AAAI Conference on Artificial Intelligence (2015)Google Scholar
  17. 17.
    Christakis, N.A., Fowler, J.H.: Social contagion theory: examining dynamic social networks and human behavior. Statist. Med. 32(4), 556–577 (2013)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Counts, S., De Choudhury, M., Diesner, J., Gilbert, E., Gonzalez, M., Keegan, B., Naaman, M., Wallach, H.: Computational social science: Cscw in the social media Era. In: Proceedings of the Companion Publication of the 17th ACM Conference on Computer Supported Cooperative Work and Social Computing, pp. 105–108. ACM (2014)Google Scholar
  19. 19.
    Deutsch, M., Gerard, H.B.: A study of normative and informational social influences upon individual judgment. J. Abnorm. Soc. Psychol. 51(3), 629 (1955)CrossRefGoogle Scholar
  20. 20.
    Diebold, F.X.: Elements of forecasting. Citeseer, Ohio (1998)Google Scholar
  21. 21.
    Diebold, F.X.: Forecasting. Department of Economics, University of Pennsylvania (2015).
  22. 22.
    Eichler, M.: Graphical modelling of multivariate time series. Probab. Theor. Relat. Fields 153(1–2), 233–268 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Eichler, M.: Causal inference with multiple time series: principles and problems. Philos. Trans. Royal Soc. London A Math. Phys. Eng. Sci. 371(1997), 20110613 (2013)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Ghosh, R., Lerman, K.: Predicting influential users in online social networks. In: Proceedings of KDD Workshop on Social Network Analysis (SNA-KDD), July 2010Google Scholar
  25. 25.
    González-Bailón, S., Borge-Holthoefer, J., Rivero, A., Moreno, Y.: The dynamics of protest recruitment through an online network. Sci. Rep. 1, 197 (2011)Google Scholar
  26. 26.
    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, pp. 241–250. ACM (2010)Google Scholar
  27. 27.
    Greenberg, J.: Advertisers don’t like facebook’s reactions. They love them. WIRED (2016).
  28. 28.
    Hlaváčková-Schindler, K., Paluš, M., Vejmelka, M., Bhattacharya, J.: Causality detection based on information-theoretic approaches in time series analysis. Phys. Rep. 441(1), 1–46 (2007)CrossRefGoogle Scholar
  29. 29.
    Katz, E., Lazarsfeld, P.F.: Personal Influence, The Part Played by People in the Flow of Mass Communications. The Free Press, New York (1955)Google Scholar
  30. 30.
    Kelman, H.C.: Processes of opinion change. Public Opin. Q. 25(1), 57–78 (1961)CrossRefGoogle Scholar
  31. 31.
    Kempe, David, Kleinberg, Jon, Tardos, Éva: Influential nodes in a diffusion model for social networks. In: Caires, Luís, Italiano, Giuseppe, F., Monteiro, Luís, Palamidessi, Catuscia, Yung, Moti (eds.) ICALP 2005. LNCS, vol. 3580, pp. 1127–1138. Springer, Heidelberg (2005). doi: 10.1007/11523468_91 CrossRefGoogle Scholar
  32. 32.
    Kilduff, M., Chiaburu, D.S., Menges, J.I.: Strategic use of emotional intelligence in organizational settings: Exploring the dark side. Res. Organ. Behav. 30, 129–152 (2010)CrossRefGoogle Scholar
  33. 33.
    Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabsi, A.L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., Jebara, T., King, G., Macy, M., Roy, D., Van Alstyne, M.: Computational social science. Science 323(5915), 721–723 (2009). CrossRefGoogle Scholar
  34. 34.
    Mason, W., Vaughan, J.W., Wallach, H.: Computational social science and social computing. Mach. Learn. 95(3), 257 (2014)MathSciNetCrossRefGoogle Scholar
  35. 35.
    Morriss, P.: Power: A Philosophical Analysis. Manchester University Press, Manchester (1987)Google Scholar
  36. 36.
    Nickerson, D.W.: Is voting contagious? evidence from two field experiments. Am. Polit. Sci. Rev. 102(01), 49–57 (2008)CrossRefGoogle Scholar
  37. 37.
    Pearl, J.: Causal inference in statistics: An overview. Stat. Surv. 3, 96–146 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  38. 38.
    Pearl, J.: Causality. Cambridge University Press, Cambridge (2009)CrossRefzbMATHGoogle Scholar
  39. 39.
    Rogers, E.M.: Diffusion of Innovations. Simon and Schuster, New York (2003)Google Scholar
  40. 40.
    Runge, J.: Quantifying information transfer and mediation along causal pathways in complex systems. Phys. Rev. E 92(6), 62829 (2015)CrossRefGoogle Scholar
  41. 41.
    Salganik, M.J., Dodds, P.S., Watts, D.J.: Experimental study of inequality and unpredictability in an artificial cultural market. Science 311(5762), 854–856 (2006)CrossRefGoogle Scholar
  42. 42.
    Shalizi, C.: Advanced Data Analysis from an Elementary Point of View. Cambridge University Press, New York (2013)Google Scholar
  43. 43.
    Shalizi, C.R., Thomas, A.C.: Homophily and contagion are generically confounded in observational social network studies. Sociol. Methods Res. 40(2), 211–239 (2011)MathSciNetCrossRefGoogle Scholar
  44. 44.
    Sharma, A., Cosley, D.: Distinguishing between personal preferences and social influence in online activity feeds. In: Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work and Social Computing, pp. 1091–1103. CSCW 2016, NY, USA (2016).
  45. 45.
    Sharma, A., Hofman, J.M., Watts, D.J.: Estimating the causal impact of recommendation systems from observational data. In: Proceedings of the Sixteenth ACM Conference on Economics and Computation, pp. 453–470. ACM (2015)Google Scholar
  46. 46.
    Sperber, D.: Explaining culture: A naturalistic approach. Cambridge University Press, New York (1996)Google Scholar
  47. 47.
    Spirtes, P.: Introduction to causal inference. J. Mach. Learn. Res. 11(May), 1643–1662 (2010)MathSciNetzbMATHGoogle Scholar
  48. 48.
    Wallach, H.: Computational social science: Toward a collaborative future. In: Computational Social Science: Discovery and Prediction (2016)Google Scholar
  49. 49.
    Watts, D.: Challenging the influentials hypothesis. WOMMA Measuring Word Mouth 3(4), 201–211 (2007)Google Scholar
  50. 50.
    Watts, D.J.: Everything is obvious: * Once you know the answer. Crown Business (2011)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.University of SouthamptonSouthamptonUK

Personalised recommendations