Quantitative Marketing and Economics

, Volume 10, Issue 1, pp 111–150 | Cite as

Impact of social network structure on content propagation: A study using YouTube data

  • Hema YoganarasimhanEmail author


We study how the size and structure of the local network around a node affects the aggregate diffusion of products seeded by it. We examine this in the context of YouTube, the popular video-sharing site. We address the endogeneity problems common to this setting by using a rich dataset and a careful estimation methodology. We empirically demonstrate that the size and structure of an author’s local network is a significant driver of the popularity of videos seeded by her, even after controlling for observed and unobserved video characteristics, unobserved author characteristics, and endogenous network formation. Our findings are distinct from those in the peer effects literature, which examines neighborhood effects on individual behavior, since we document the causal relationship between a node’s local network position and the global diffusion of products seeded by it. Our results provide guidelines for identifying seeds that provide the best return on investment, thereby aiding managers conducting buzz marketing campaigns on social media forums. Further, our study sheds light on the other substantive factors that affect video consumption on YouTube.


Social network YouTube Diffusion Social media User-generated content Network structure Online video Social influence Contagion 


C36 C33 M3 O33 L14 



Discussions with Dina Mayzlin, Harikesh Nair, Sridhar Naryanan, and Jiwoong Shin have greatly improved this paper. Comments from the Editor, Greg Allenby, and two anonymous reviewers have also helped the paper considerably. Finally, thanks are also due to the participants of the PhD Student Research Workshop at the Yale School of Management 2009, NASMEI 2009, UT Dallas Forms Conference 2009, Marketing Science Conference 2010, Marketing Dynamics Conference 2010, Stanford Marketing Seminar 2010, Haas Marketing Seminar 2010, and University of Washington Marketing Seminar 2011, for their feedback.


  1. Acemoglu, D., & Robinson, J. (2001). A theory of political transition. The American Economic Review, 91(4), 938–963.CrossRefGoogle Scholar
  2. Anderson, T. W., & Hsaio, C. (1981). Estimation of dynamic models with error components. Journal of the American Statistical Association, 76(375), 598–606.CrossRefGoogle Scholar
  3. Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58, 277–97.CrossRefGoogle Scholar
  4. Bandeira, O., & Rasul, I. (2006). Social networks and technology adoption in Northern Mozambique. The Economic Journal, 116, 869–902.CrossRefGoogle Scholar
  5. Bandiera, O., Barankay, I., & Rasul, I. (2009). Social connections and incentives in the workplace: evidence from personnel data. Econometrica., 77, 1047–94.CrossRefGoogle Scholar
  6. Barabasi, A. L., Albert, R., & Jeong, H. (2000). Scale-free characteristics of random networks: the topology of the world wide web. Physica A, 281, 69–77.CrossRefGoogle Scholar
  7. Barry, K. (2009). Ford bets the fiesta on social networking. Wired.Google Scholar
  8. Bass, F. M. (1969). A new product growth model for consumer durables. Management Science, 15, 215–227.CrossRefGoogle Scholar
  9. Bertrand, M., Luttmer, E. F. P., & Mullainathan, S. (2000). Network effects and welfare cultures. Quarterly Journal of Economics, 115, 1019–1056.CrossRefGoogle Scholar
  10. Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictionsin dynamic panel data models. Journal of Econometrics., 87, 115–43.CrossRefGoogle Scholar
  11. Borgatti, S. P. G., Jones, C., & Everett, M. G. (1998). Network measures of social capital. Connections, 21, 27–36.Google Scholar
  12. Borgatti, S. P., Carley, K. M., & Krackhardt, D. (2006). On the robustness of centrality measures under conditions of imperfect data. Social Networks, 28, 124–136.CrossRefGoogle Scholar
  13. Borgatti, S. P., & Everett, M. G. (2006). A graph-theoretic perspective on centrality. Social Networks, 28, 466–84.CrossRefGoogle Scholar
  14. Bramoullé, Y., Djebbari, H., & Fortin, B. (2009). Identification of peer effects through social networks. Journal of Econometrics., 150, 41–55.CrossRefGoogle Scholar
  15. Brock, W. A., & Durlauf, S. N. (2007). Identification of binary choice models with social interactions. Journal of Econometrics, 140(1), 52–75.CrossRefGoogle Scholar
  16. Burt, R. (1995). Structural holes: The social structure of competition. Harvard University Press.Google Scholar
  17. Clark, C. C., Doraszelski, U., & Draganska, M. (2009). The effect of advertising on brand awareness and perceived quality: an empirical investigation using panel data. Quantitative Marketing and Economics, 7, 207–236.CrossRefGoogle Scholar
  18. Coleman, J. S., Katz, E., & Menzel, H. (1966). Medical innovation: A diffusion study. Indianapolis: Bobb-Merrill.Google Scholar
  19. Durlauf, S., Johnson, P., & Temple, J. (2005). Growth econometrics. In P. Aghion & S. Durlauf (Eds.), Handbook of econometric growth (Vol. 1A, pp. 555–677). Amsterdam: North-Holland.Google Scholar
  20. Everett, M. G., & Borgatti, S. P. (2005). Ego-network betweenness. Social Networks, 27(1), 31–38.CrossRefGoogle Scholar
  21. Feed Company. (2008). Viral video marketing survey: The agency perspective.Google Scholar
  22. Feld, S. L. (1991). Why your friends have more friends than you do. The American Journal of Sociology, 96(6), 1464–77.CrossRefGoogle Scholar
  23. Freeman, L. C. (1979). Centrality in social networks: a conceptual clarification. Social Networks. pp. 1–21.Google Scholar
  24. Friedkin, N. E. (1991). Theoretical foundations for centrality measures. The American Journal of Sociology, 96, 1478–1504.CrossRefGoogle Scholar
  25. Greenberg, K. (2010). Ford fiesta movement shifts into high gear. Marketing Daily.Google Scholar
  26. Girvan, M., & Newman, M. E. J. (2002). Community structure in social and biological networks. Proceedings of the National Academy of Science of the United States of America, 99(12), 7821–26.CrossRefGoogle Scholar
  27. Goldenberg, J., Sangman, H., Lehmann, D. R., & Hong, J. W. (2009). The role of hubs in the adoption process. Journal of Marketing, 73, 1–13.CrossRefGoogle Scholar
  28. Gould, R. V., & Fernandez, R. M. (1989). Structures of mediation: A formal approach to brokerage in transaction networks. In C. C. Clogg & A. Arbor (Eds.), Sociological methodology (pp. 89–126). MI: Blackwell.Google Scholar
  29. Granovetter, M. (1973). The strength of weak ties. The American Journal of Sociology, 78(6), 1360–80.CrossRefGoogle Scholar
  30. Hansen, B. E. (2008). Econometrics. available at:
  31. Hartmann, W. R., Manchanda, P., Nair, H., Bothner, M., Dodds, P., Godes, D., et al. (2008). Modeling social interactions: identification, empirical methods and policy implications. Marketing Letters, 19(3).Google Scholar
  32. Hitwise Experian. (2010). Top 20 sites and engines. available at:
  33. Katona, Z., Zubcsek, P. P., & Sarvary, M. (2009). Network effects and personal influences: Diffusion of an online social network. Working paper.Google Scholar
  34. Katz, E., & Lazarsfeld, P. F. (1955). Personal influence: The part played by people in the flow of mass communications. Glencoe: Free.Google Scholar
  35. Mahajan, V., Muller, E., & Wind, Y. (2000). New product diffusion models: From theory to practice. In V. Majan, E. Muller, & Y. Wind (Eds.), New product diffusion models. Boston: Kluwer.Google Scholar
  36. Manski, C. F. (1993). Identification of endogenous social effects: the reflection problem. The Review of Economic Studies, 60(3), 531–42.CrossRefGoogle Scholar
  37. McCracken, G. (2010). How Ford got social marketing right. The Conversation, Harvard Business Review.Google Scholar
  38. McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks.Google Scholar
  39. Mislove, A., Marcon, M., Gummadi, K., Druschel, P., & Bhattacharjee, B. (2007). Measurement and Analysis of Online Social Networks. In Proceedings of the 5th ACM/USENIX Internet Measurement Conference, San Diego, CA.Google Scholar
  40. Moynihan, R. (2008). Key opinion leaders: independent experts or drug representatives in disguise. British Medical Journal, 336, 1402–03.CrossRefGoogle Scholar
  41. Nair, H., Manchanda, P., & Bhatia, T. (2009). Asymmetric social interactions in physician prescription behavior: The role of opinion leaders. Working paper.Google Scholar
  42. Nickell, S. (1981). Biases in dynamic models with fixed effects. Econometrica, 39, 359–87.Google Scholar
  43. Nielson Online. (2010). Nielsen net ratings April 2010.Google Scholar
  44. Rogers, E. M. (2003). Diffusion of innovations (5th ed.). New York: Free.Google Scholar
  45. Sacerdote, B. (2001). Peer effects with random assignment: results for Dartmouth roommates. Quarterly Journal of Economics, 116, 681–704.CrossRefGoogle Scholar
  46. Stephen A. T., & Toubia, O. (2010). Deriving value from social commerce networks. forthcoming Journal of Marketing Research.Google Scholar
  47. Tajfel, H., & Turner, J. C. (1986). The social identity theory of inter-group behavior. In S. Worchel & W. G. Austin (Eds.), Psychology of intergroup relations (2nd ed., pp. 7–24). Chicago: Nelson-Hall.Google Scholar
  48. Tauchen, G. (1986). Statistical properties of generalized method of moments estimators of structural parameters obtained from financial market data. Journal of Business and Economic Statistics, 4(4), 397–416.CrossRefGoogle Scholar
  49. Trogdon, J., Nonnemaker, J., & Pais, J. (2008). Peer effects in adolescent overweight. Journal of Health Economics, 27(5), 1388–1399.CrossRefGoogle Scholar
  50. Tucker, C. (2008). Identifying formal and informal influence in technology adoption with network externalities. Management Science, 55(12), 2024–2039.CrossRefGoogle Scholar
  51. Valente, T. W., & Pumpuang, P. (2007). Identifying opinion leaders to promote behavior changes. Health Education & Behavior, 34, 881–96.CrossRefGoogle Scholar
  52. Watts, D. J., & Dodds, P. S. (2007). Influentials, networks, and public opinion formation. Journal of Consumer Research, 34, 441–58.CrossRefGoogle Scholar
  53. Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘Small-World’ networks. Nature, 393(4), 440–42.CrossRefGoogle Scholar
  54. Windmeijer, F. (2005). A finite sample correction for the variance of linear efficient two-step GMM estimators. Journal of Econometrics, 126(1), 25–51.CrossRefGoogle Scholar
  55. Woolridge, J. (2008). Introductory econometrics: A modern approach. 4th ed., South-Western College Pub.Google Scholar
  56. Ziliak, J. P. (1997). Efficient estimation with panel data when instruments are predetermined: an empirical comparison of moment-condition estimators. Journal of Business and Economic Statistics, 15(4), 419–31.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Graduate School of ManagementUniversity of California DavisDavisUSA

Personalised recommendations