Advertisement

Peer and Authority Pressure in Information-Propagation Models

  • Aris Anagnostopoulos
  • George Brova
  • Evimaria Terzi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6911)

Abstract

Existing models of information diffusion assume that peer influence is the main reason for the observed propagation patterns. In this paper, we examine the role of authority pressure on the observed information cascades. We model this intuition by characterizing some nodes in the network as “authority” nodes. These are nodes that can influence large number of peers, while themselves cannot be influenced by peers. We propose a model that associates with every item two parameters that quantify the impact of the peer and the authority pressure on the item’s propagation. Given a network and the observed diffusion patterns of the item, we learn these parameters from the data and characterize the item as peer- or authority-propagated. We also develop a randomization test that evaluates the statistical significance of our findings and makes our item characterization robust to noise. Our experiments with real data from online media and scientific-collaboration networks indicate that there is a strong signal of authority pressure in these networks.

Keywords

Randomization Test Collaboration Network Information Item Online Medium News Site 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Amatriain, X., Lathia, N., Pujol, J.M., Kwak, H., Oliver, N.: The wisdom of the few: A collaborative filtering approach based on expert opinions from the web. In: SIGIR (2009)Google Scholar
  2. 2.
    Anagnostopoulos, A., Kumar, R., Mahdian, M.: Influence and correlation in social networks. In: KDD (2008)Google Scholar
  3. 3.
    Aral, S., Muchnik, L., Sundararajan, A.: Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. Proceedings of the National Academy of Sciences, PNAS 106(51) (2009)Google Scholar
  4. 4.
    Bass, F.M.: A new product growth model for consumer durables. Management Science 15, 215–227 (1969)CrossRefzbMATHGoogle Scholar
  5. 5.
    Caccioppo, J.T., Fowler, J.H., Christakis, N.A.: Alone in the crowd: The structure and spread of loneliness in a large social network. Journal of Personality and Social Psychology 97(6), 977–991 (2009)CrossRefGoogle Scholar
  6. 6.
    Christakis, N., Fowler, J.: Connected: The surprising power of our social networks and how they shape our lives. Back Bay Books (2010)Google Scholar
  7. 7.
    Fowler, J.H., Christakis, N.A.: The dynamic spread of happiness in a large social network: Longitudinal analysis over 20 years in the framingham heart study. British Medical Journal 337 (2008)Google Scholar
  8. 8.
    Gomez-Rodriguez, M., Leskovec, J., Krause, A.: Inferring networks of diffusion and influence. In: KDD (2010)Google Scholar
  9. 9.
    Granovetter, M.: Threshold models of collective behavior. The American Journal of Sociology 83, 1420–1443 (1978)CrossRefGoogle Scholar
  10. 10.
    Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: KDD (2003)Google Scholar
  11. 11.
    Knowledge and Data Engineering Group, University of Kassel, Benchmark Folksonomy Data from BibSonomy, Version of June 30th (2007)Google Scholar
  12. 12.
    Leskovec, J., Backstrom, L., Kleinberg, J.M.: Meme-tracking and the dynamics of the news cycle. In: KDD (2009)Google Scholar
  13. 13.
    Onnela, J.-P., Reed-Tsochas, F.: Spontaneous emergence of social influence in online systems. Proceedings of the National Academy of Sciences, PNAS (2010)Google Scholar
  14. 14.
    Rosenquist, J.N., Fowler, J.H., Christakis, N.A.: Social network determinants of depression. Molecular Psychiatry 16(3), 273–281 (2010)CrossRefGoogle Scholar
  15. 15.
    Wu, S., Hofman, J.M., Mason, W.A., Watts, D.J.: Who says what to whom on twitter. In: WWW, pp. 705–714 (2011)Google Scholar
  16. 16.
    Yang, J., Leskovec, J.: Modeling information diffusion in implicit networks. In: ICDM (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Aris Anagnostopoulos
    • 1
  • George Brova
    • 2
  • Evimaria Terzi
    • 2
  1. 1.Department of Computer and System SciencesSapienza University of RomeItaly
  2. 2.Computer Science DepartmentBoston UniversityUSA

Personalised recommendations