Influence

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Influence and Passivity in Social Media
Daniel M. Romero
Cornell University Center for Applied Mathematics Ithaca, New York, USA

Wojciech Galuba
Distributed Information Systems Lab EPFL Lausanne, Switzerland

dmr239@cornell.edu Sitaram Asur
Social Computing Lab HP Labs Palo Alto, California, USA

wojciech.galuba@epfl.ch Bernardo A. Huberman
Social Computing Lab HP Labs Palo Alto,California, USA

sitaram.asur@hp.com 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 contentto 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 ofURL 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.

bernardo.huberman@hp.com
ing companies and celebrities eager to exploit this vast new medium. As a result, ideas, opinions, and products compete with all other content for the scarce attention of theuser community. In spite of the seemingly chaotic fashion with which all these interactions take place, certain topics manage to get an inordinate amount of attention, thus bubbling to the top in terms of popularity and contributing to new trends and to the public agenda of the community. How this happens in a world where crowdsourcing dominates is still an unresolved problem, but there isconsiderable consensus on the fact that two aspects of information transmission seem to be important in determining which content receives attention. One aspect is the popularity and status of given members of these social networks, which is measured by the level of attention they receive in the form of followers who create links to their accounts to automatically receive the content they generate. Theother aspect is the influence that these individuals wield, which is determined by the actual propagation of their content through the network. This influence is determined by many factors, such as the novelty and resonance of their messages with those of their followers and the quality and frequency of the content they generate. Equally important is the passivity of members of the network whichprovides a barrier to propagation that is often hard to overcome. Thus gaining knowledge of the identity of influential and least passive people in a network can be extremely useful from the perspectives of viral marketing, propagating one’s point of view, as well as setting which topics dominate the public agenda. In this paper, we analyze the propagation of web links on Twitter over time tounderstand how attention to given users and their influence is determined. We devise a general model for influence using the concept of passivity in a social network and develop an efficient algorithm similar to the HITS algorithm [14] to quantify the influence of all the users in the network. Our influence measure utilizes both the structural properties of the network as well as the diffusion behavior amongusers. The influence of a user thus depends not only on the size of the influenced audience, but also on their passivity. This differentiates our measure of influence from earlier ones, which were primarily based on individual statistical properties such as the number of fol-

1. INTRODUCTION
The explosive growth of Social Media has provided millions of people the opportunity to create and share...
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