Advertisement

Game-Theoretic Models of Information Overload in Social Networks

  • Christian Borgs
  • Jennifer Chayes
  • Brian Karrer
  • Brendan Meeder
  • R. Ravi
  • Ray Reagans
  • Amin Sayedi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6516)

Abstract

We study the effect of information overload on user engagement in an asymmetric social network like Twitter. We introduce simple game-theoretic models that capture rate competition between celebrities producing updates in such networks where users non-strategically choose a subset of celebrities to follow based on the utility derived from high quality updates as well as disutility derived from having to wade through too many updates. Our two variants model the two behaviors of users dropping some potential connections (followership model) or leaving the network altogether (engagement model). We show that under a simple formulation of celebrity rate competition, there is no pure strategy Nash equilibrium under the first model. We then identify special cases in both models when pure rate equilibria exist for the celebrities: For the followership model, we show existence of a pure rate equilibrium when there is a global ranking of the celebrities in terms of the quality of their updates to users. This result also generalizes to the case when there is a partial order consistent with all the linear orders of the celebrities based on their qualities to the users. Furthermore, these equilibria can be computed in polynomial time. For the engagement model, pure rate equilibria exist when all users are interested in the same number of celebrities, or when they are interested in at most two. Finally, we also give a finite though inefficient procedure to determine if pure equilibria exist in the general case of the followership model.

Keywords

Social Network Nash Equilibrium Bipartite Graph Dependency Graph Engagement Model 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
  2. 2.
    Eppler, M.J., Mengis, J.: The Concept of Information Overload: A Review of Literature from Organization Science, Accounting, Marketing, MIS, and Related Disciplines. The Information Society 20(5), 325–344 (2004)CrossRefGoogle Scholar
  3. 3.
    Huberman, B., Romero, D., Wu, F.: Social networks that matter: Twitter under the microscope. First Monday 14(1) (2009)Google Scholar
  4. 4.
    Jackson, M.O.: Social and Economic Networks. Princeton University Press, Princeton (2009)zbMATHGoogle Scholar
  5. 5.
    Sattinger, M.: Value of an Additional Firm in Monopolistic Competition. The Review of Economic Studies 51(2), 321–332 (1984)CrossRefzbMATHGoogle Scholar
  6. 6.
    Clay Shirkey on information overload versus filter failure, video from Web 2.0 Expo NY (2010), http://www.boingboing.net/2010/01/31/clay-shirky-on-infor.html
  7. 7.
    Doyle, P.G., Snell, J.L.: Random walks and electric networks. The Mathematical Association of America (1984)Google Scholar
  8. 8.
    Horn, R.A., Johnson, C.R.: Matrix analysis. Cambridge University Press, Cambridge (1990)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Christian Borgs
    • 1
  • Jennifer Chayes
    • 1
  • Brian Karrer
    • 1
    • 2
  • Brendan Meeder
    • 1
    • 3
  • R. Ravi
    • 1
    • 3
  • Ray Reagans
    • 1
    • 4
  • Amin Sayedi
    • 1
    • 3
  1. 1.Microsoft Research New EnglandCambridgeUSA
  2. 2.University of MichiganAnn ArborUSA
  3. 3.Carnegie Mellon UniversityUSA
  4. 4.MIT Sloan School of ManagementUSA

Personalised recommendations