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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)

Abstract

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.

Keywords

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.

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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

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