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Knowledge and Information Systems

, Volume 37, Issue 3, pp 555–584 | Cite as

Topic-aware social influence propagation models

  • Nicola Barbieri
  • Francesco BonchiEmail author
  • Giuseppe Manco
Regular Paper

Abstract

The study of influence-driven propagations in social networks and its exploitation for viral marketing purposes has recently received a large deal of attention. However, regardless of the fact that users authoritativeness, expertise, trust and influence are evidently topic-dependent, the research on social influence has surprisingly largely overlooked this aspect. In this article, we study social influence from a topic modeling perspective. We introduce novel topic-aware influence-driven propagation models that, as we show in our experiments, are more accurate in describing real-world cascades than the standard (i.e., topic-blind) propagation models studied in the literature. In particular, we first propose simple topic-aware extensions of the well-known Independent Cascade and Linear Threshold models. However, these propagation models have a very large number of parameters which could lead to overfitting. Therefore, we propose a different approach explicitly modeling authoritativeness, influence and relevance under a topic-aware perspective. Instead of considering user-to-user influence, the proposed model focuses on user authoritativeness and interests in a topic, leading to a drastic reduction in the number of parameters of the model. We devise methods to learn the parameters of the models from a data set of past propagations. Our experimentation confirms the high accuracy of the proposed models and learning schemes.

Keywords

Social influence Topic modeling Topic-aware propagation model  Viral marketing 

Notes

Acknowledgments

This research was partially supported by the Torres Quevedo Program of the Spanish Ministry of Science and Innovation and partially funded by the European Union 7th Framework Programme (FP7/2007-2013) under Grant No. 270239 (ARCOMEM).

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Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Nicola Barbieri
    • 1
  • Francesco Bonchi
    • 1
    Email author
  • Giuseppe Manco
    • 2
  1. 1.Yahoo! ResearchBarcelonaSpain
  2. 2.ICAR-CNRCosenzaItaly

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