Predicting Information Diffusion in Social Networks Using Content and User’s Profiles

  • Cédric Lagnier
  • Ludovic Denoyer
  • Eric Gaussier
  • Patrick Gallinari
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7814)


Predicting the diffusion of information on social networks is a key problem for applications like Opinion Leader Detection, Buzz Detection or Viral Marketing. Many recent diffusion models are direct extensions of the Cascade and Threshold models, initially proposed for epidemiology and social studies. In such models, the diffusion process is based on the dynamics of interactions between neighbor nodes in the network (the social pressure), and largely ignores important dimensions as the content of the piece of information diffused. We propose here a new family of probabilistic models that aims at predicting how a content diffuses in a network by making use of additional dimensions: the content of the piece of information diffused, user’s profile and willingness to diffuse. These models are illustrated and compared with other approaches on two blog datasets. The experimental results obtained on these datasets show that taking into account the content of the piece of information diffused is important to accurately model the diffusion process.


Social Network Social Pressure Threshold Model Incoming Neighbor Linear Threshold 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Abrahamson, E., Rosenkopf, L.: Social network effects on the extent of innovation diffusion: A computer simulation. Organization Science 8, 289–309 (1997)CrossRefGoogle Scholar
  2. 2.
    Borodin, A., Filmus, Y., Oren, J.: Threshold models for competitive influence in social networks, pp. 1–15 (October 2010)Google Scholar
  3. 3.
    Burton, K., Java, A., Soboroff, I.: The ICWSM 2009 Spinn3r Dataset. In: The Third Annual Conference on Weblogs and Social Media, ICWSM 2009 (May 2009)Google Scholar
  4. 4.
    Dodds, P., Watts, D.: Universal Behavior in a Generalized Model of Contagion. Physical Review Letter 92, 218701 (2004)CrossRefGoogle Scholar
  5. 5.
    Goldenberg, J., Libai, B., Muller, E.: Talk of the Network: A Complex Systems Look at the Underlying Process of Word-of-Mouth. In: Marketing Letters, pp. 211–223 (2001)Google Scholar
  6. 6.
    Granovetter, M.: Threshold Models of Collective Behavior. American Journal of Sociology 83, 1420–1443 (1978)CrossRefGoogle Scholar
  7. 7.
    Granovetter, M., Soong, R.: Threshold models of diversity: Chinese restaurants, residential segregation, and the spiral of silence. Sociological Methodology 18, 69–104 (1988)CrossRefGoogle Scholar
  8. 8.
    Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 137–146. ACM Press (2003)Google Scholar
  9. 9.
    Kimura, M., Saito, K., Nakano, R.: Extracting influential nodes for information diffusion on a social network. In: Proceedings of the National Conference on Artificial Intelligence, vol. 22(2), p. 1371 (2007)Google Scholar
  10. 10.
    Leskovec, J., Backstrom, L., Kleinberg, J.: Meme-tracking and the dynamics of the news cycle. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2009, pp. 497–506. ACM (2009)Google Scholar
  11. 11.
    Liben-Nowell, D., Kleinberg, J.: Tracing information flow on a global scale using Internet chain-letter data. Proceedings of the National Academy of Sciences 105, 4633–4638 (2008)CrossRefGoogle Scholar
  12. 12.
    López-Pintado, D., Watts, D.J.: Social Influence, Binary Decisions and Collective Dynamics. Rationality and Society 20, 399–443 (2008)CrossRefGoogle Scholar
  13. 13.
    Macy, M.W.: Chains of Cooperation: Threshold Effects in Collective Action. American Sociological Review 56, 730–747 (1991)CrossRefGoogle Scholar
  14. 14.
    Manning, C.D., Raghavan, P., Schtze, H.: Introduction to Information Retrieval. Cambridge University Press, New York (2008)zbMATHCrossRefGoogle Scholar
  15. 15.
    Newman, M.E.J.: The structure and function of complex networks. SIAM Review 45(2), 167–256 (2003)MathSciNetzbMATHCrossRefGoogle Scholar
  16. 16.
    Richardson, M., Domingos, P.: Mining knowledge-sharing sites for viral marketing. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2002, pp. 61–70. ACM (2002)Google Scholar
  17. 17.
    Rodriguez, M.G., Balduzzi, D., Schölkopf, B.: Uncovering the temporal dynamics of diffusion networks. In: Getoor, L., Scheffer, T. (eds.) Proceedings of the 28th International Conference on Machine Learning, ICML 2011, pp. 561–568. ACM (2011)Google Scholar
  18. 18.
    Saito, K., Kimura, M., Ohara, K., Motoda, H.: Learning Continuous-Time Information Diffusion Model for Social Behavioral Data Analysis. In: Zhou, Z.-H., Washio, T. (eds.) ACML 2009. LNCS, vol. 5828, pp. 322–337. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  19. 19.
    Saito, K., Nakano, R., Kimura, M.: Prediction of Information Diffusion Probabilities for Independent Cascade Model. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds.) KES 2008, Part III. LNCS (LNAI), vol. 5179, pp. 67–75. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  20. 20.
    Saito, K., Ohara, K., Yamagishi, Y., Kimura, M., Motoda, H.: Learning Diffusion Probability Based on Node Attributes in Social Networks. In: Kryszkiewicz, M., Rybinski, H., Skowron, A., Raś, Z.W. (eds.) ISMIS 2011. LNCS, vol. 6804, pp. 153–162. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  21. 21.
    Valente, T.W.: Network Models of the Diffusion of Innovations. Quantitative Methods in Communication Subseries. Hampton Press, NJ (1995)Google Scholar
  22. 22.
    Yang, J., Leskovec, J.: Modeling information diffusion in implicit networks. In: IEEE International Conference on Data Mining, Stanford InfoLab (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Cédric Lagnier
    • 1
  • Ludovic Denoyer
    • 2
  • Eric Gaussier
    • 1
  • Patrick Gallinari
    • 2
  1. 1.Université Grenoble 1, LIGGrenobleFrance
  2. 2.Université Pierre et Marie Curie, LIP6ParisFrance

Personalised recommendations