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Social Ties and User-Generated Content: Evidence from an Online Social Network

Author

Listed:
  • Shriver, Scott K.

    (Columbia University)

  • Nair, Harikesh S.

    (Stanford University)

  • Hofstetter, Reto

    (University of St Gallen)

Abstract

We use variation in wind speeds at surfing locations in Switzerland as exogenous shifters of users' propensity to post content about their surfing activity onto an online social network. We exploit this variation to test whether users' online content generation activity has a causal effect on their social ties. Under weak monotonicity assumptions, we also estimate nonparametric bounds on the causal effect of user's social ties in turn on their content generation activity. Economically significant causal effects of the type above can produce positive feedback that generates local network effects in content generation. We find evidence for such network effects. We argue this feedback generates a multiplier effect on interventions that subsidize tie formation. We use our estimates to measure the ROI from such interventions and discuss implications for the site's monetization strategy. Our empirical strategy provides one way to address a significant identification challenge with online social network data that the observed network structure is endogenous to the actions taken by agents on the network. Augmenting the model of agent's actions with a model for the network structure requires solving a formidable network formation game. Our approach to this problem is to conduct inference with an incomplete model of network formation under weak assumptions that deliver informative bounds on the causal effects of interest, while avoiding taking a strong stand on a specific model of network formation.

Suggested Citation

  • Shriver, Scott K. & Nair, Harikesh S. & Hofstetter, Reto, 2011. "Social Ties and User-Generated Content: Evidence from an Online Social Network," Research Papers 2083, Stanford University, Graduate School of Business.
  • Handle: RePEc:ecl:stabus:2083
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    as
    1. Jacob M. Markman & Eric A. Hanushek & John F. Kain & Steven G. Rivkin, 2003. "Does peer ability affect student achievement?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(5), pages 527-544.
    2. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    3. Charles F. Manski, 1997. "Monotone Treatment Response," Econometrica, Econometric Society, vol. 65(6), pages 1311-1334, November.
    4. Kleibergen, Frank & Paap, Richard, 2006. "Generalized reduced rank tests using the singular value decomposition," Journal of Econometrics, Elsevier, vol. 133(1), pages 97-126, July.
    5. Dhar, Vasant & Chang, Elaine A., 2009. "Does Chatter Matter? The Impact of User-Generated Content on Music Sales," Journal of Interactive Marketing, Elsevier, vol. 23(4), pages 300-307.
    6. Sridhar Narayanan & Harikesh S. Nair, 2011. "Estimating Causal Installed-Base Effects: A Bias-Correction Approach," Working Papers 11-22, NET Institute.
    7. Chrysanthos Dellarocas, 2006. "Strategic Manipulation of Internet Opinion Forums: Implications for Consumers and Firms," Management Science, INFORMS, vol. 52(10), pages 1577-1593, October.
    8. Gal Oestreicher-Singer & Arun Sundararajan, 2012. "The Visible Hand? Demand Effects of Recommendation Networks in Electronic Markets," Management Science, INFORMS, vol. 58(11), pages 1963-1981, November.
    9. Bramoullé, Yann & Djebbari, Habiba & Fortin, Bernard, 2009. "Identification of peer effects through social networks," Journal of Econometrics, Elsevier, vol. 150(1), pages 41-55, May.
    10. Charles F. Manski, 2000. "Economic Analysis of Social Interactions," Journal of Economic Perspectives, American Economic Association, vol. 14(3), pages 115-136, Summer.
    11. Friestad, Marian & Wright, Peter, 1994. "The Persuasion Knowledge Model: How People Cope with Persuasion Attempts," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 21(1), pages 1-31, June.
    12. Michael Braun & André Bonfrer, 2011. "Scalable Inference of Customer Similarities from Interactions Data Using Dirichlet Processes," Marketing Science, INFORMS, vol. 30(3), pages 513-531, 05-06.
    13. Federico Ciliberto & Elie Tamer, 2009. "Market Structure and Multiple Equilibria in Airline Markets," Econometrica, Econometric Society, vol. 77(6), pages 1791-1828, November.
    14. Paulo Albuquerque & Polykarpos Pavlidis & Udi Chatow & Kay-Yut Chen & Zainab Jamal, 2012. "Evaluating Promotional Activities in an Online Two-Sided Market of User-Generated Content," Marketing Science, INFORMS, vol. 31(3), pages 406-432, May.
    15. Philip A. Haile & Elie Tamer, 2003. "Inference with an Incomplete Model of English Auctions," Journal of Political Economy, University of Chicago Press, vol. 111(1), pages 1-51, February.
    16. Blundell, Richard & Griffith, Rachel & Windmeijer, Frank, 2002. "Individual effects and dynamics in count data models," Journal of Econometrics, Elsevier, vol. 108(1), pages 113-131, May.
    17. Yann Bramoullé & Bernard Fortin, 2009. "The Econometrics of Social Networks," Cahiers de recherche 0913, CIRPEE.
    18. Lee, Lung-fei, 2007. "Identification and estimation of econometric models with group interactions, contextual factors and fixed effects," Journal of Econometrics, Elsevier, vol. 140(2), pages 333-374, October.
    19. Nair, Harikesh S. & Manchanda, Puneet & Bhatia, Tulikaa, 2006. "Asymmetric Peer Effects in Physician Prescription Behavior: The Role of Opinion Leaders," Research Papers 1970, Stanford University, Graduate School of Business.
    20. Charles F. Manski & John V. Pepper, 2000. "Monotone Instrumental Variables, with an Application to the Returns to Schooling," Econometrica, Econometric Society, vol. 68(4), pages 997-1012, July.
    21. Hema Yoganarasimhan, 2012. "Impact of social network structure on content propagation: A study using YouTube data," Quantitative Marketing and Economics (QME), Springer, vol. 10(1), pages 111-150, March.
    22. Anindya Ghose & Sang Pil Han, 2011. "An Empirical Analysis of User Content Generation and Usage Behavior on the Mobile Internet," Management Science, INFORMS, vol. 57(9), pages 1671-1691, September.
    23. Dae-Yong Ahn & Jason A. Duan & Carl F. Mela, 2011. "An Equilibrium Model of User Generated Content," Working Papers 11-13, NET Institute, revised Dec 2011.
    24. Zsolt Katona & Miklos Sarvary, 2008. "Network Formation and the Structure of the Commercial World Wide Web," Marketing Science, INFORMS, vol. 27(5), pages 764-778, 09-10.
    25. Wesley Hartmann & Puneet Manchanda & Harikesh Nair & Matthew Bothner & Peter Dodds & David Godes & Kartik Hosanagar & Catherine Tucker, 2008. "Modeling social interactions: Identification, empirical methods and policy implications," Marketing Letters, Springer, vol. 19(3), pages 287-304, December.
    26. Juanjuan Zhang, 2010. "The Sound of Silence: Observational Learning in the U.S. Kidney Market," Marketing Science, INFORMS, vol. 29(2), pages 315-335, 03-04.
    27. Xiaoquan (Michael) Zhang & Feng Zhu, 2011. "Group Size and Incentives to Contribute: A Natural Experiment at Chinese Wikipedia," American Economic Review, American Economic Association, vol. 101(4), pages 1601-1615, June.
    28. Dina Mayzlin & Hema Yoganarasimhan, 2012. "Link to Success: How Blogs Build an Audience by Promoting Rivals," Management Science, INFORMS, vol. 58(9), pages 1651-1668, September.
    29. Pradeep K. Chintagunta & Shyam Gopinath & Sriram Venkataraman, 2010. "The Effects of Online User Reviews on Movie Box Office Performance: Accounting for Sequential Rollout and Aggregation Across Local Markets," Marketing Science, INFORMS, vol. 29(5), pages 944-957, 09-10.
    30. Chamberlain, Gary, 1992. "Sequential Moment Restrictions in Panel Data: Comment," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(1), pages 20-26, January.
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    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
    • L11 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Production, Pricing, and Market Structure; Size Distribution of Firms
    • L12 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Monopoly; Monopolization Strategies
    • L16 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Industrial Organization and Macroeconomics; Macroeconomic Industrial Structure
    • L68 - Industrial Organization - - Industry Studies: Manufacturing - - - Appliances; Furniture; Other Consumer Durables
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing

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