Feedback effects between similarity and social influence in online communities

ABSTRACT

A fundamental open question in the analysis of social networks is to understand the interplay between similarity and social ties. People are similar to their neighbors in a social network for two distinct reasons: first, they grow to resemble their current friends due to social influence; and second, they tend to form new links to others who are already like them, a process often termed selection by sociologists. While both factors are present in everyday social processes, they are in tension: social influence can push systems toward uniformity of behavior, while selection can lead to fragmentation. As such, it is important to understand the relative effects of these forces, and this has been a challenge due to the difficulty of isolating and quantifying them in real settings.

We develop techniques for identifying and modeling the interactions between social influence and selection, using data from online communities where both social interaction and changes in behavior over time can be measured. We find clear feedback effects between the two factors, with rising similarity between two individuals serving, in aggregate, as an indicator of future interaction -- but with similarity then continuing to increase steadily, although at a slower rate, for long periods after initial interactions. We also consider the relative value of similarity and social influence in modeling future behavior. For instance, to predict the activities that an individual is likely to do next, is it more useful to know the current activities of their friends, or of the people most similar to them?

References

1. L. Backstrom, D. Huttenlocher, J. Kleinberg, and X. Lan. Group formation in large social networks: membership, growth, and evolution. KDD, 2006.
2. N. Berger, C. Borgs, J. T. Chayes, and A. Saberi. On the spread of viruses on the Internet. ACM Symposium on Discrete Algorithms, 2005.
3. R. Burke. Hybrid recommender systems: Surveys and experiments. User Modeling and User-Adapted Interaction, 12(4), 2002.
4. M. Cataldo, P. Wagstrom, J. Herbsleb, and K. Carley. Identification of coordination requirements: Implications for the design of collaboration and awareness tools. In CSCW '06, 2006.
5. D. Cosley, D. Frankowski, L. Terveen, and J. Riedl. SuggestBot: Using intelligent task routing to help people find work in Wikipedia. IUI, 2007.
6. P. Domingos. Mining social networks for viral marketing. IEEE Intelligent Systems, 20(1), 2005.
7. N. E. Friedkin. A Structural Theory of Social Influence. Cambridge University Press, 1998.
8. C. Gutwin and S. Greenberg. The importance of awareness for team cognition in distributed collaboration. In E. Salas and S. M. Fiore, editors, Team cognition, APA Press, 2004.
9. W. Hill, L. Stead, M. Rosenstein, and G. Furnas. Recommending and evaluating choices in a virtual community of use. In CHI '95, pages 194--201, 1995.
10. P. Holme and M. E. J. Newman. Nonequilibrium phase transition in the coevolution of networks and opinions. Physical Review E, 74:056108, 2006.
11. G. Kossinets and D. J. Watts. Empirical analysis of an evolving social network. Science, 311(2006).
12. R. Kumar, J. Novak, and A. Tomkins. Structure and evolution of online social networks. KDD, 2006.
13. P. Lazarsfeld and R. Merton. Friendship as a social process: A substantive and methodological analysis. In M. Bergen, T. Abel, and C. Page, editors, Freedom and Control in Modern Society. Van Nostrand, 1954.
14. J. Leskovec, L. A. Adamic, and B. A. Huberman. The dynamics of viral marketing. ACM EC, 2006.
15. M. Macy, J. Kitts, A. Flache, and S. Benard. Polarization in dynamic networks. In R. Breiger, K. Carley, P. Pattison (eds.), Dynamic Social Network Modeling and Analysis, Natl. Acad. Press, 2003.
16. M. McPherson, L. Smith-Lovin, and J. Cook. Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27, 2001.
17. R. Pemantle. A survey of random processes with reinforcement. Probability Surveys, 4:1--79, 2007.
18. P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl. Grouplens: An open architecture for collaborative filtering of netnews. CSCW, 1994.
19. E. Rogers. Diffusion of Innovations, Free Press 1995.
20. G. Salton. Introduction to Modern Information Retrieval (McGraw-Hill Computer Science Series). McGraw-Hill Companies, September 1983.
21. U. Shardanand, P. Maes. Social information filtering: Algorithms for automating word of mouth?. CHI'95.
22. D. Strang, S. Soule. Diffusion in organizations and social movements. Ann. Rev. Soc. 1998
23. B. Stvilia, M. B. Twidale, L. C. Smith, and L. Gasser. Assessing information quality of a community-based encyclopedia. In F. Naumann, M. Gertz, and S. E. Madnick, editors, IQ. MIT, 2005.
24. M. Van Alstyne, E. Brynjolfsson. Global Village or CyberBalkans: Modeling and Measuring Integration of Electronic Communities. Mgmt. Sci., in press.
25. F. B. Viegas, M. Wattenberg, J. Kriss, and F. van Ham. Talk before you type: Coordination in Wikipedia. In HICSS 2007, pages 78--87, 2007.
26. J. Voss. Measuring Wikipedia. In International Conference of the International Society for Scientometrics and Informetrics, 2005.
27. S. Wasserman, K. Faust. Social Network Analysis. Cambridge Univ. Press, 1994.

Index Terms

• Information systems

  • Information systems applications

    • Data mining