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Predicting Information Diffusion in Social Networks using Content and User's Profiles

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 con- tent diffuses in a network by making use of additional dimensions: the content of the piece of information diffused, user's profile and willing- ness 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.
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Cédric Lagnier, Ludovic Denoyer, Eric Gaussier, Patrick Gallinari. Predicting Information Diffusion in Social Networks using Content and User's Profiles. 35th European Conference on IR Research, ECIR 2013, Mar 2013, Moscou, Russia. pp.74-85, ⟨10.1007/978-3-642-36973-5_7⟩. ⟨hal-00881524⟩

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