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Influence and Passivity in Social Media

  • Daniel M. Romero
  • Wojciech Galuba
  • Sitaram Asur
  • Bernardo A. Huberman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6913)

Abstract

The ever-increasing amount of information flowing through Social Media forces the members of these networks to compete for attention and influence by relying on other people to spread their message. A large study of information propagation within Twitter reveals that the majority of users act as passive information consumers and do not forward the content to the network. Therefore, in order for individuals to become influential they must not only obtain attention and thus be popular, but also overcome user passivity. We propose an algorithm that determines the influence and passivity of users based on their information forwarding activity. An evaluation performed with a 2.5 million user dataset shows that our influence measure is a good predictor of URL clicks, outperforming several other measures that do not explicitly take user passivity into account. We demonstrate that high popularity does not necessarily imply high influence and vice-versa.

Keywords

Social Network Social Medium Twitter User Social Graph Passivity Score 
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 2011

Authors and Affiliations

  • Daniel M. Romero
    • 1
  • Wojciech Galuba
    • 2
  • Sitaram Asur
    • 3
  • Bernardo A. Huberman
    • 3
  1. 1.Center for Applied MathematicsCornell UniversityIthacaUSA
  2. 2.Distributed Information Systems LabEPFLLausanneSwitzerland
  3. 3.Social Computing LabHP LabsPalo AltoUSA

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