Machine Learning and Knowledge Discovery in Databases

Volume 6913 of the series Lecture Notes in Computer Science pp 18-33

Influence and Passivity in Social Media

  • Daniel M. RomeroAffiliated withCenter for Applied Mathematics, Cornell University
  • , Wojciech GalubaAffiliated withDistributed Information Systems Lab, EPFL
  • , Sitaram AsurAffiliated withSocial Computing Lab, HP Labs
  • , Bernardo A. HubermanAffiliated withSocial Computing Lab, HP Labs

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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.