Confluence: conformity influence in large social networks

ABSTRACT

Conformity is a type of social influence involving a change in opinion or behavior in order to fit in with a group. Employing several social networks as the source for our experimental data, we study how the effect of conformity plays a role in changing users' online behavior. We formally define several major types of conformity in individual, peer, and group levels. We propose Confluence model to formalize the effects of social conformity into a probabilistic model. Confluence can distinguish and quantify the effects of the different types of conformities. To scale up to large social networks, we propose a distributed learning method that can construct the Confluence model efficiently with near-linear speedup. Our experimental results on four different types of large social networks, i.e., Flickr, Gowalla, Weibo and Co-Author, verify the existence of the conformity phenomena. Leveraging the conformity information, Confluence can accurately predict actions of users. Our experiments show that Confluence significantly improves the prediction accuracy by up to 5-10% compared with several alternative methods.

References

1. A. Anagnostopoulos, R. Kumar, and M. Mahdian. Influence and correlation in social networks. In KDD'08, pages 7--15, 2008.
2. E. Bakshy, D. Eckles, R. Yan, and I. Rosenn. Social influence in social advertising: evidence from field experiments. In EC'12, pages 146--161, 2012.
3. B. D. Bernheim. A theory of conformity. Journal of Political Economy, 1027(5):841--877, 1994.
4. R. M. Bond, C. J. Fariss, J. J. Jones, A. D. I. Kramer, C. Marlow, J. E. Settle, and J. H. Fowler. A 61-million-person experiment in social influence and political mobilization. Nature, 489:295--298, 2012.
5. R. S. Burt. Structural Holes: The Social Structure of Competition. Harvard University Press, 1992.
6. W. Chen, Y. Wang, and S. Yang. Efficient influence maximization in social networks. In KDD'09, pages 199--207, 2009.
7. E. Cho, S. A. Myers, and J. Leskovec. Friendship and mobility: user movement in location-based social networks. In KDD'11, pages 1082--1090, 2011.
8. R. B. Cialdini and N. J. Goldstein. Social influence: Compliance and conformity. Annual Review of Psychology, 55:591--621, 2004.
9. D. Crandall, D. Cosley, D. Huttenlocher, J. Kleinberg, and S. Suri. Feedback effects between similarity and social influence in online communities. In KDD'08, pages 160--168, 2008.
10. P. Domingos and M. Richardson. Mining the network value of customers. In KDD'01, pages 57--66, 2001.
11. D. Easley and J. Kleinberg. Networks, Crowds, and Markets: Reasoning about a Highly Connected World. Cambridge University Press, 2010.
12. M. Gomez-Rodriguez, J. Leskovec, and A. Krause. Inferring networks of diffusion and influence. In KDD'10, pages 1019--1028, 2010.
13. A. Goyal, F. Bonchi, and L. V. Lakshmanan. Learning influence probabilities in social networks. In WSDM'10, pages 241--250, 2010.
14. M. Granovetter. The strength of weak ties. American Journal of Sociology, 78(6):1360--1380, 1973.
15. J. M. Hammersley and P. Clifford. Markov field on finite graphs and lattices. Unpublished manuscript, 1971.
16. G. Karypis and V. Kumar. MeTis: Unstrctured Graph Partitioning and Sparse Matrix Ordering System, Version 4.0, Sept. 1998.
17. H. C. Kelman. Compliance, identification, and internalization: Three processes of attitude change. Journal of Conflict Resolution, 2(1):51--60, 1958.
18. D. Kempe, J. Kleinberg, and E. Tardos. Maximizing the spread of influence through a social network. In KDD'03, pages 137--146, 2003.
19. F. R. Kschischang, B. J. Frey, and H. andrea Loeliger. Factor graphs and the sum-product algorithm. IEEE TOIT, 47:498--519, 2001.
20. T. La Fond and J. Neville. Randomization tests for distinguishing social influence and homophily effects. In WWW'10, pages 601--610, 2010.
21. J. D. Lafferty, A. McCallum, and F. C. N. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In ICML'01, pages 282--289, 2001.
22. H. Li, S. S. Bhowmick, and A. Sun. Casino: towards conformity-aware social influence analysis in online social networks. In CIKM'11, pages 1007--1012, 2011.
23. L. Liu, J. Tang, J. Han, and S. Yang. Learning influence from heterogeneous social networks. Data Mining and Knowledge Discovery, 25(3):511--544, 2012.
24. T. Lou and J. Tang. Mining structural hole spanners through information diffusion in social networks. In WWW'13, pages 837--848, 2013.
25. A. K. McCallum. Mallet: A machine learning for language toolkit. http://mallet.cs.umass.edu, 2002.
26. K. P. Murphy, Y. Weiss, and M. I. Jordan. Loopy belief propagation for approximate inference: An empirical study. In UAI'99, pages 467--475, 1999.
27. M. E. J. Newman. Fast algorithm for detecting community structure in networks. Phys. Rev. E, 69(066133), 2004.
28. M. Richardson and P. Domingos. Mining knowledge-sharing sites for viral marketing. In KDD'02, pages 61--70, 2002.
29. X. Song, Y. Chi, K. Hino, and B. L. Tseng. Identifying opinion leaders in the blogosphere. In CIKM'06, pages 971--974, 2007.
30. C. Tan, J. Tang, J. Sun, Q. Lin, and F. Wang. Social action tracking via noise tolerant time-varying factor graphs. In KDD'10, pages 1049--1058, 2010.
31. J. Tang, T. Lou, and J. Kleinberg. Inferring social ties across heterogeneous networks. In WSDM'12, pages 743--752, 2012.
32. J. Tang, J. Sun, C. Wang, and Z. Yang. Social influence analysis in large-scale networks. In KDD'09, pages 807--816, 2009.
33. J. Tang, J. Zhang, L. Yao, J. Li, L. Zhang, and Z. Su. Arnetminer: Extraction and mining of academic social networks. In KDD'08, pages 990--998, 2008.
34. W. Wiegerinck. Variational approximations between mean field theory and the junction tree algorithm. In UAI'00, pages 626--633, 2000.
35. J. Zhang, B. Liu, J. Tang, T. Chen, and J. Li. Social influence locality for modeling retweeting behaviors. In IJCAI'13, 2013.

Index Terms

• Applied computing

  • Law, social and behavioral sciences

    • Sociology

• Information systems

  • Information retrieval