The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.05 - September/October (2011 vol.26)
pp: 91-96
Sinan Aral , New York University
Dylan Walker , New York University
ABSTRACT
<p>Identifying causal estimates of peer-to-peer influence in networks is critical to marketing strategy, public policy, and beyond. Unfortunately, separating correlation from causation in networked data is complicated. The authors argue that randomized experimentation in networks, made possible by the digitization of human interaction at population scale, can dramatically improve our understanding of the ebb and flow of market trends, product adoption and diffusion, the spread of health behaviors, the productivity of information workers, and whether particular individuals in a social network have a disproportionate amount of influence on the system. They also discuss some of the complications that arise when conducting randomized experiments in networks by describing an experiment designed to test how different viral product design strategies affect peer influence and social contagion in new product diffusion.</p>
INDEX TERMS
intelligent systems, cyber-physical-social systems, peer influence, social contagion, social networks, endogeneity, causality, randomized experiments
CITATION
Sinan Aral, Dylan Walker, "Identifying Social Influence in Networks Using Randomized Experiments", IEEE Intelligent Systems, vol.26, no. 5, pp. 91-96, September/October 2011, doi:10.1109/MIS.2011.89
REFERENCES
1. D. Lazer et al., "Computational Social Science," Science, vol. 323, no. 5915, 2009, pp. 721–722.
2. S. Aral and M.W. Van Alstyne, "The Diversity-Bandwidth Tradeoff," Am. J. Sociology, vol. 117, no. 1, 2011, pp. 90–171.
3. N.A. Christakis and J.H. Fowler, "The Spread of Obesity in a Large Social Network over 32 Years," New England J. Medicine, vol. 357, no. 4, 2007, pp. 370–379.
4. R. Lyons, "The Spread of Evidence-Poor Medicine via Flawed Social-Network Analysis," Statistics, Politics, and Policy, vol. 2, no. 1, 2011, article 2.
5. S. Aral and D. Walker, "Creating Social Contagion through Viral Product Design: A Randomized Trial of Peer Influence in Networks," Management Science, 2011doi:10.1287/mnsc.1110.1421, http://mansci.journal.informs.org/content/ early/2011/08/03mnsc.1110.1421.full.pdf .
6. S. Aral, L. Muchnik, and A. Sundararajan, "Distinguishing Influence-Based Contagion from Homophily-Driven Diffusion in Dynamic Networks," Proc. Nat'l Academy Sciences, vol. 106, 2009, pp. 21544–21549.
7. C. Shalizi and A.C. Thomas, "Homophily and Contagion Are Generically Confounded in Observational Social Network Studies," Sociological Methods and Research, vol. 40, no. 2, 2011, pp. 211–239.
8. D. Godes and D. Mayzlin, "Using Online Conversations to Study Word-of-Mouth Communication," Marketing Science, vol. 23, no. 4, 2004, pp. 545–560.
9. C. Van den Bulte and G.L. Lilien, "Medical Innovation Revisited: Social Contagion versus Marketing Effort," Am. J. Sociology, vol. 106, no. 5, 2001, pp. 1409–1435.
10. C. Van den Bulte and R. Iyengar, "Tricked by Truncation: Spurious Duration Dependence and Social Contagion in Hazard Models," Marketing Science, vol. 30, no. 2, 2011, pp. 233–248.
11. C.F. Manski, "Identification Problems in the Social Sciences," Sociological Methodology, vol. 23, 1993, pp. 1–56.
12. S. Aral, "Identifying Social Influence: A Comment on Opinion Leadership and Social Contagion in New Product Diffusion," Marketing Science, vol. 30, no. 2, 2001, pp. 217–223.
13. Y. Bramoulle, H. Djebbari, and B. Fortin, "Identification of Peer Effects through Social Networks," J. Econometrics, vol. 150, no. 1, 2009, pp. 41–55.
14. T. Snijders, C. Steglich, and M. Schweinberger, "Modeling the Co-evolution of Networks and Behavior," Longitudinal Models in the Behavioral and Related Sciences, K. van Montfort, J. Oud, and A. Satorra eds., Lawrence Erlbaum, 2006.
15. C. Tucker,, "Identifying Formal and Informal Influence in Technology Adoption with Network Externalities," Management Science, vol. 54, no. 12, 2008, pp. 2024–2038.
16. A. Ghose and S.-P. Han, "An Empirical Analysis of User Content Generation and Usage Behavior in Mobile Media," working paper, Stern School of Business, New York Univ., 2010.
17. E. Duflo, R. Glennerster, and M. Kremer, "Using Randomization in Development Economics Research: A Toolkit," Handbook of Developmental Economics, Elsevier, 2006.
18. D. Centola, "The Spread of Behavior in an Online Social Network Experiment," Science, vol. 329, no. 5996, 2010, pp. 1194–1197.
19. M. Kearns, S. Suri, and N. Montfort, "An Experimental Study of the Coloring Problem on Human Subject Networks," Science, vol. 313, no. 5788, 2006, pp. 824–827.
20. S. Suri and D.J. Watts, "Cooperation and Contagion in Web-Based, Networked Public Goods Experiments," PLoS One, vol. 6, no. 3, 2011, p. e16836.
21. S. Aral and D. Walker, "An Experimental Method for Identifying Influential and Susceptible Members of Online Social Networks," working paper, Stern School of Business, New York Univ., 2011.
22. E. Duflo and E. Saez, "The Role of Information and Social Interactions in Retirement Plan Decisions: Evidence From a Randomized Experiment," Quarterly J. Economics, vol. 118, 2003, pp. 815–842.
23. S. Leider et al., "Directed Altruism and Enforced Reciprocity in Social Networks," Quarterly J. Economics, vol. 124, no. 4, 2009, pp. 1815–1851.
24. S. Aral, E. Brynjolfsson, and M. Van Alstyne, "Productivity Effects of Information Diffusion in Networks," Proc. 28th Ann. Int'l Conf. Information Systems, 2007, paper 17; http://aisel.aisnet.org/icis200717.
19 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool