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Twitter Adoption in Congress

Author

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  • Chi Feng

    (University of Toronto Rotman School of Management)

  • Yang Nathan

    (University of Toronto)

Abstract

We study the early adoption of Twitter in the 111th House of Representatives. Our main objective is to determine whether successes of past adopters have the tendency to speed up Twitter adoption, where past success is defined as the average followers per Tweet - a common measure of "Twitter success" - among all prior adopters. The data suggests that accelerated adoption can be associated with favorable past outcomes: increasing the average number of followers per Tweet among past adopters by a standard deviation (of eight followers per Tweet) accelerates the adoption time by about 112 days. This acceleration effect is weaker for those who already have adopted Facebook and those who have access to information about a large number of past adopters. We later find a positive relationship between an adopter's own success and the success of adopters preceding him/her. Thus, there may exist benefits associated with adopting Twitter based on past successes of others. In general, the patterns we find are consistent with predictions generated by a simple model of adoption delay with learning.

Suggested Citation

  • Chi Feng & Yang Nathan, 2011. "Twitter Adoption in Congress," Review of Network Economics, De Gruyter, vol. 10(1), pages 1-46, March.
  • Handle: RePEc:bpj:rneart:v:10:y:2011:i:1:n:3
    DOI: 10.2202/1446-9022.1255
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    1. Caplin, Andrew & Leahy, John, 1998. "Miracle on Sixth Avenue: Information Externalities and Search," Economic Journal, Royal Economic Society, vol. 108(446), pages 60-74, January.
    2. Oriana Bandiera & Imran Rasul, 2006. "Social Networks and Technology Adoption in Northern Mozambique," Economic Journal, Royal Economic Society, vol. 116(514), pages 869-902, October.
    3. Charles F. Manski, 1993. "Identification of Endogenous Social Effects: The Reflection Problem," Review of Economic Studies, Oxford University Press, vol. 60(3), pages 531-542.
    4. Fabian Waldinger, 2012. "Peer Effects in Science: Evidence from the Dismissal of Scientists in Nazi Germany," Review of Economic Studies, Oxford University Press, vol. 79(2), pages 838-861.
    5. Bruce Sacerdote, 2001. "Peer Effects with Random Assignment: Results for Dartmouth Roommates," The Quarterly Journal of Economics, Oxford University Press, vol. 116(2), pages 681-704.
    6. Timothy G. Conley & Christopher R. Udry, 2010. "Learning about a New Technology: Pineapple in Ghana," American Economic Review, American Economic Association, vol. 100(1), pages 35-69, March.
    7. Chi, Feng & Yang, Nathan, 2010. "Twitter in Congress: Outreach vs Transparency," MPRA Paper 23597, University Library of Munich, Germany, revised 22 Jun 2010.
    8. Jason M. Fletcher, 2010. "Social interactions and smoking: evidence using multiple student cohorts, instrumental variables, and school fixed effects," Health Economics, John Wiley & Sons, Ltd., vol. 19(4), pages 466-484, April.
    9. Charles F. Manski, 2000. "Economic Analysis of Social Interactions," Journal of Economic Perspectives, American Economic Association, vol. 14(3), pages 115-136, Summer.
    10. Andersen, Thomas Barnebeck, 2009. "E-Government as an anti-corruption strategy," Information Economics and Policy, Elsevier, vol. 21(3), pages 201-210, August.
    11. Goolsbee, Austan & Klenow, Peter J, 2002. "Evidence on Learning and Network Externalities in the Diffusion of Home Computers," Journal of Law and Economics, University of Chicago Press, vol. 45(2), pages 317-343, October.
    12. Christopher J. Malloy, 2005. "The Geography of Equity Analysis," Journal of Finance, American Finance Association, vol. 60(2), pages 719-755, April.
    13. Bikhchandani, Sushil & Hirshleifer, David & Welch, Ivo, 1992. "A Theory of Fads, Fashion, Custom, and Cultural Change in Informational Cascades," Journal of Political Economy, University of Chicago Press, vol. 100(5), pages 992-1026, October.
    14. Graham, Bryan S. & Hahn, Jinyong, 2005. "Identification and estimation of the linear-in-means model of social interactions," Economics Letters, Elsevier, vol. 88(1), pages 1-6, July.
    15. H. Peyton Young, 2009. "Innovation Diffusion in Heterogeneous Populations: Contagion, Social Influence, and Social Learning," American Economic Review, American Economic Association, vol. 99(5), pages 1899-1924, December.
    16. Forman, Chris & Goldfarb, Avi & Greenstein, Shane, 2005. "How did location affect adoption of the commercial Internet? Global village vs. urban leadership," Journal of Urban Economics, Elsevier, vol. 58(3), pages 389-420, November.
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    2. Leighton Vaughan Williams & James Reade, 2014. "Prediction Markets, Twitter and Bigotgate," Economics Discussion Papers em-dp2014-09, Department of Economics, University of Reading.
    3. G. Lappas & A. Triantafillidou & P. Yannas, 2019. "Members of European Parliament (MEPs) on Social Media: Understanding the Underlying Mechanisms of Social Media Adoption and Popularity," The Review of Socionetwork Strategies, Springer, vol. 13(1), pages 55-77, June.
    4. Gary E. Hollibaugh Jr. & Adam J. Ramey & Jonathan D. Klingler, 2018. "Welcome to the Machine: A Model of Legislator Personality and Communications Technology Adoption," SAGE Open, , vol. 8(3), pages 21582440188, September.
    5. Nathan Yang, 2011. "An Empirical Model of Industry Dynamics with Common Uncertainty and Learning from the Actions of Competitors," Working Papers 11-16, NET Institute.
    6. Hollibaugh, Gary E. & Klingler, Jonathan & Ramey, Adam, 2015. "Tentative Decisions," IAST Working Papers 15-29, Institute for Advanced Study in Toulouse (IAST).
    7. Byungho Park & Moon Young Kang & Jiwon Lee, 2020. "Sustainable Political Social Media Marketing: Effects of Structural Features in Plain Text Messages," Sustainability, MDPI, Open Access Journal, vol. 12(15), pages 1-10, July.
    8. Ferihan Polat & Özlem Özdeşim Subay, 2016. "The Use of Twitter by Politicians During June 2015 and November 2015 General Elections the Case of PDP," European Journal of Multidisciplinary Studies Articles, European Center for Science Education and Research, vol. 1, January-A.
    9. Fabio Giudice & Rocco Caferra & Piergiuseppe Morone, 2020. "COVID-19, the Food System and the Circular Economy: Challenges and Opportunities," Sustainability, MDPI, Open Access Journal, vol. 12(19), pages 1-15, September.

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