article

Social Networks and the Diffusion of User-Generated Content: Evidence from YouTube

Authors:

Anjana Susarla,

Yong TanAuthors Info & Claims

Information Systems Research, Volume 23, Issue 1

Pages 23 - 41

https://doi.org/10.1287/isre.1100.0339

Published: 01 March 2012 Publication History

Abstract

This paper is motivated by the success of YouTube, which is attractive to content creators as well as corporations for its potential to rapidly disseminate digital content. The networked structure of interactions on YouTube and the tremendous variation in the success of videos posted online lends itself to an inquiry of the role of social influence. Using a unique data set of video information and user information collected from YouTube, we find that social interactions are influential not only in determining which videos become successful but also on the magnitude of that impact. We also find evidence for a number of mechanisms by which social influence is transmitted, such as (i) a preference for conformity and homophily and (ii) the role of social networks in guiding opinion formation and directing product search and discovery. Econometrically, the problem in identifying social influence is that individuals' choices depend in great part upon the choices of other individuals, referred to as the reflection problem. Another problem in identification is to distinguish between social contagion and user heterogeneity in the diffusion process. Our results are in sharp contrast to earlier models of diffusion, such as the Bass model, that do not distinguish between different social processes that are responsible for the process of diffusion. Our results are robust to potential self-selection according to user tastes, temporal heterogeneity and the reflection problem. Implications for researchers and managers are discussed.

References

[1]

Angrist, J. and Krueger, A., "Does compulsory school attendance affect schooling and earnings?," Quart. J. Econom., v106, pp. 979-1014, 1991.

[2]

Armstrong, C. P. and Sambamurthy, V., "Information technology assimilation in firms: The influence of senior leadership and IT infrastructures," Inform. Systems Res., v10, pp. 304-327, 1999.

Digital Library

[3]

Bala, V. and Goyal, S., "Learning from neighbors," Rev. Econom. Stud., v65, pp. 595-621, 1998.

[4]

Baluja, S., Seth, R., Sivakumar, D., Jing, Y., Yagnik, J., Kumar, S., Ravichandran, D. and Aly, M., "Video suggestion and discovery for YouTube: Taking random walks through the view graph," Proc. 17th Internat. Conf. World Wide Web, pp. 895-904, 2008.

Digital Library

[5]

Bandiera, O. and Rasul, I., "Social networks and technology adoption in Northern Mozambique," Econom. J., v116, pp. 862-902, 2006.

[6]

Bass, F., "A new product growth for model consumer durables," Management Sci., v15, pp. 215-227, 1969.

Digital Library

[7]

Bemmaor, A. C. and Lee, J. C., "The impact of heterogeneity and ill-conditioning on diffusion model parameter estimates," Marketing Sci., v21, pp. 209-220, 2002.

Digital Library

[8]

Bernheim, D. A., "A theory of conformity," J. Political Econom., v102, pp. 841-877, 1994.

[9]

Bhattacharjee, S., Gopal, R. D., Lertwachara, K., Marsden, J. R. and Telang, R., "The effect of digital sharing technologies on music markets," Management Sci., v53, pp. 1359-1374, 2007.

Digital Library

[10]

Bikhchandani, S., Hirshleifer, D. and Welch, I., "A theory of fads, fashion, custom, and cultural change as informational cascades," J. Political Econom., v100, pp. 992-1026, 1992.

[11]

Bonacich, "Power and centrality: A family of measures," Amer. J. Sociol., v92, pp. 1170-1182, 1987.

[12]

Borgatti, S. P., "Centrality and network flow," Soc. Networks, v27, pp. 55-71, 2005.

[13]

Borgatti, S. P. and Everett, M. G., "A graph-theoretic framework for classifying centrality measures," Soc. Networks, v28, pp. 466-484, 2006.

[14]

Borgatti, S. P., Everett, M. G. and Freeman, L. C., Ucinet for Windows: Software for Social Network Analysis, Analytic Technologies, Harvard, MA, 2002.

[15]

Boulding, W. and Christen, M., "Sustainable pioneering advantage? Profit implications of market entry order," Marketing Sci., v22, pp. 371-392, 2003.

Digital Library

[16]

Bramoulle, Y., Djebbari, H. and Fortin, B., "Identification of peer effects through social networks," J. Econometrics, v150, pp. 41-55, 2009.

[17]

Burt, R., "Social contagion and innovation: Cohesion versus structural equivalence," Amer. J. Sociol., v92, pp. 1287-1335, 1987.

[18]

Burt, R., Structural Holes: The Social Structure of Competition, Harvard University Press, Cambridge, MA, 1992.

[19]

Centola, D. and Macy, M., "Complex contagions and the weakness of long ties," Amer. J. Sociol., v113, pp. 702-734, 2007.

[20]

Cha, M. H., Kwak, P., Rodriguez, Y. A. and Moon, S., "I Tube, YouTube, everybody Tubes: Analyzing the world's largest user generated content video system," Proc. ACM Special Interest Group Data Comm. (SIGCOMM) Internet Measurement Conf., 2007.

Digital Library

[21]

Chevalier, J. and Mayzlin, D., "The effect of word of mouth online: Online book reviews," J. Marketing Res., v43, pp. 345-354, 2006.

[22]

Coleman, J. S., Katz, E. and Menzel, H., Medical Innovation: Diffusion of a Medical Drug Among Doctors, Bobbs-Merrill, Indianapolis, 1966.

[23]

Crane, R. and Sornette, D., "Robust dynamic classes revealed by measuring the response function of a social system," Proc. Natl. Acad. Sci., v105, pp. 15649-15653, 2008.

[24]

Dewan, S. and Ramaprasad, J., "Consumer blogging and music sampling," 2008.

[25]

Dodds, P. S. and Watts, D. J., "Universal behavior in a generalized model of contagion," Phys. Rev. Lett., v92, 2004.

[26]

Dodds, P. S. and Watts, D. J., "A generalized model of social and biological contagion," J. Theoret. Biol., v232, pp. 587-604, 2005.

[27]

Dodds, P. S. and Watts, D. J., "Influentials, networks, and public opinion formation," J. Consumer Res., v34, pp. 441-458, 2007.

[28]

Easley, D. and Kleinberg, J., Networks, Crowds, and Markets: Reasoning About a Highly Connected World, Cambridge University Press, Cambridge, UK, 2010.

Digital Library

[29]

Fichman, R. G. and Zmud, R. W., "The diffusion and assimilation of information technology innovations," Framing the Domains of IT Management: Projecting the Future Through the Past, Pinnaflex Educational Resources, Inc., Cincinnati, OH, 2000.

[30]

Forman, C., Ghose, A. and Wiesenfeld, B., "Examining the relationship between reviews and sales: The role of reviewer identity information," Inform. Systems Res., v19, pp. 291-313, 2008.

Digital Library

[31]

Freeman, L. C., "Centrality in social networks: A conceptual clarification," Soc. Networks, v1, pp. 215-239, 1979.

[32]

Gelman, A. and Hill, J., Data Analysis Using Regression and Mulitlevel/Hierarchical Models, Cambridge University Press, Cambridge, UK, 2007.

[33]

Gladwell, M., The Tipping Point: How Little Things Can Make a Big Difference, Little Brown, Boston, 2000.

[34]

Granovetter, M., "The strength of weak ties," Amer. J. Sociol., v78, pp. 1360-1380, 1973.

[35]

Hausman, J. A. and Taylor, W. E., "Panel data and unobservable individual effects," Econometrica, v49, pp. 1377-1398, 1981.

[36]

Hendricks, K. and Sorenson, A., "Information and the skewness of music sales," J. Political Econom., v117, pp. 324-369, 2009.

[37]

Ibarra, H. and Andrews, S. B., "Power, social Influence, and sense making: Effects of network centrality and proximity on employee perceptions," Admin. Sci. Quart., v38, pp. 277-303, 1993.

[38]

Kalish, S., "A new product adoption model with price, advertising and uncertainty," Management Sci., v31, pp. 1569-1585, 1985.

Digital Library

[39]

Kraut, R. E., Rice, R. E., Cool, C. and Fish, R. S., "Varieties of social influence: The role of utility and norms in the success of a new communication medium," Organ. Sci., v9, pp. 437-453, 1998.

Digital Library

[40]

Liu, H., Maes, P. and Davenport, G., "Unraveling the taste fabric of social networks," Internat. J. Semantic Web Inform. Systems, v2, pp. 42-71, 2006.

[41]

Mahajan, V., Muller, E. and Bass, F. M., "New product diffusion models in marketing: A review and directions for research," J. Marketing, v54, pp. 1-26, 1990.

[42]

Manchanda, P., Xie, Y. and Youn, N., "The role of targeted communication and contagion in new product adoption," Marketing Sci., v27, pp. 950-961, 2008.

[43]

Manski, C. F., "Identification of endogenous social effects: The reflection problem," Rev. Econom. Stud., v60, pp. 531-542, 1993.

[44]

Nelson, P., "Information and consumer behavior," J. Political Econom., v78, pp. 311-329, 1970.

[45]

Newman, M. E. J., "The structure and function of complex networks," SIAM Rev., v45, pp. 167-256, 2003.

Digital Library

[46]

Oestreicher-Singer, G. and Sundararajan, A., "The visible hand of social networks in electronic markets," 2008.

[47]

Parameswaran, M. and Whinston, A. B., "Social computing: An overview," Comm. Assoc. Inform. Systems, v19, pp. 762-780, 2007.

[48]

Peck, R. S., Zhou, L. Y., Anthony, V. B. and Madhukar, K., "Consumer Internet, Bear Stearns equity research report," 2008.

[49]

Putnam, R., Bowling Alone: The Collapse and Revival of American Community, Simon and Schuster, New York, 2000.

Digital Library

[50]

Raymond, E., The Cathedral and the Bazaar, O'Reilly, Sebastopol, CA, 2001.

Digital Library

[51]

Reagans, R. E. and Zuckerman, E. W., "Why knowledge does not equal power: The network redundancy trade-off," Indust. Corporate Change, v17, pp. 903-944, 2008.

[52]

Resnick, P., Zeckhauser, R., Friedman, E. and Kuwabara, K., "Reputation systems," Comm. ACM, v43, pp. 45-48, 2000.

Digital Library

[53]

Rogers, E., Diffusion of Innovations, The Free Press, New York, 1995.

[54]

Rogers, E. M. and Kincaid, L. D., Communication Networks, Free Press, New York, 1981.

[55]

Sacerdote, B., "Peer effects with random assignment: Results for Dartmouth roommates," Quart. J. Econom., v116, pp. 681-704, 2001.

[56]

Salganik, M., Dodds, P. S. and Watts, D., "Experimental study of inequality and unpredictability in an artificial cultural market," Science, v311, pp. 854-856, 2006.

[57]

Strang, D. and Tuma, N. B., "Spatial and temporal heterogeneity in diffusion," Amer. J. Sociol., v99, pp. 614-639, 1993.

[58]

Sundararajan, A., "Local network effects and complex network structure," Berkeley Electronic J. Theoret. Econom., v7, 2007.

[59]

Szabo, G. and Huberman, B. A., "Predicting the popularity of online content," Comm. ACM, 2009.

Digital Library

[60]

Talukdar, D., Sudhir, K. and Ainslie, A., "Investigating new product diffusion across products and countries," Marketing Sci., v21, pp. 97-114, 2002.

Digital Library

[61]

Van den Bulte, C. and Lilien, G., "Medical innovation revisited: Social contagion versus marketing effort," Amer. J. Sociol., v106, pp. 1409-1435, 2001.

[62]

Van den Bulte, C. and Stremersch, S., "Social contagion and income heterogeneity in new product diffusion: A meta-analytic test," Marketing Sci., v23, pp. 530-544, 2004.

Digital Library

[63]

Wang, F.-Y., Carley, K. M., Zeng, D. and Mao, W., "Social computing: From social informatics to social intelligence," IEEE Intelligent Systems, v22, pp. 79-83, 2007.

Digital Library

[64]

Wasserman, S. and Faust, K., Social Network Analysis: Methods and Applications, Cambridge University Press, Cambridge, UK, 1994.

[65]

Watts, D. J., "A simple model of global cascades on random networks," Proc. Natl. Acad. Sci., v99, pp. 5766-5771, 2002.

[66]

Watts, D. J., Dodds, P. S. and Newman, M. E. J., Science, v296, pp. 1302-1305, 2002.

[67]

Wejnert, B., "Integrating models of diffusion of innovations: A conceptual framework," Annual Rev. Sociol., pp. 297-326, 2002.

[68]

Wells, J., "It's crunch time," Globe Mail, 2009.

[69]

Wooldridge, J. M., Econometric Analysis of Cross Section and Panel Data, MIT Press, Cambridge, MA, 2002.

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cover image Information Systems Research

Information Systems Research Volume 23, Issue 1

03 2012

285 pages

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Linthicum, MD, United States

Publication History

Published: 01 March 2012

Received: 21 July 2008

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