Predicting Information Diffusion in Social Networks using Content and User's Profiles - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2013

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

Résumé

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.
Fichier principal
Vignette du fichier
UserCentricModels-novspace.pdf (150.4 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-00881524 , version 1 (08-11-2013)

Identifiants

Citer

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⟩
402 Consultations
1908 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More