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Twitter Adoption in Congress: Who Tweets First?

Author

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  • Chi, Feng
  • Yang, Nathan

Abstract

Our general objective is to characterize the recent and well publicized diffusion of Twitter among politicians in the United States 111th House of Representatives. Ultimately, Barrack Obama, Facebook and peers matter when it comes to the propensity and speed of Twitter adoption. A basic analysis of the distribution of first Tweets over time reveals clustering around the President's inauguration; which holds regardless whether the adopter is Democratic or Republican, or an incumbent or newcomer. After we characterize which representatives are most likely to adopt Twitter, we confirm the widespread belief that Facebook and Twitter are indeed complementary technology. Given their perceived desire for accessible government, a surprising result is that Republicans are more likely to adopt Twitter than Democrats. Finally, using the exact dates of each adopter's first Tweet, we demonstrate that the diffusion of Twitter is faster for those representatives with a larger number of peers already using the technology, where peers are defined by two social networks: (1) Politicians representing the same state; and (2) politicians belonging to the same committees; especially so for those in committee networks. This observed behavior can be rationalized by social learning, as the instances in which the peer effects are important correspond to the cases in which social learning is relevant.

Suggested Citation

  • Chi, Feng & Yang, Nathan, 2010. "Twitter Adoption in Congress: Who Tweets First?," MPRA Paper 23225, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:23225
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    References listed on IDEAS

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    1. Lauren Cohen & Andrea Frazzini & Christopher Malloy, 2008. "The Small World of Investing: Board Connections and Mutual Fund Returns," Journal of Political Economy, University of Chicago Press, vol. 116(5), pages 951-979, October.
    2. Davezies, Laurent & D'Haultfoeuille, Xavier & Fougère, Denis, 2006. "Identification of Peer Effects Using Group Size Variation," IZA Discussion Papers 2324, Institute of Labor Economics (IZA).
    3. 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.
    4. Laurent Davezies & Xavier D'Haultfoeuille & Denis Fougère, 2009. "Identification of peer effects using group size variation," Econometrics Journal, Royal Economic Society, vol. 12(3), pages 397-413, November.
    5. Charles F. Manski, 2000. "Economic Analysis of Social Interactions," Journal of Economic Perspectives, American Economic Association, vol. 14(3), pages 115-136, Summer.
    6. Brock, William A. & Durlauf, Steven N., 2007. "Identification of binary choice models with social interactions," Journal of Econometrics, Elsevier, vol. 140(1), pages 52-75, September.
    7. Francisco J. Buera & Alexander Monge‐Naranjo & Giorgio E. Primiceri, 2011. "Learning the Wealth of Nations," Econometrica, Econometric Society, vol. 79(1), pages 1-45, January.
    8. 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.
    9. Goldfarb, Avi & Prince, Jeff, 2008. "Internet adoption and usage patterns are different: Implications for the digital divide," Information Economics and Policy, Elsevier, vol. 20(1), pages 2-15, March.
    10. Chamley,Christophe P., 2004. "Rational Herds," Cambridge Books, Cambridge University Press, number 9780521530927, December.
    11. 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.
    12. Chamley, Christophe & Gale, Douglas, 1994. "Information Revelation and Strategic Delay in a Model of Investment," Econometrica, Econometric Society, vol. 62(5), pages 1065-1085, September.
    13. Chamley,Christophe P., 2004. "Rational Herds," Cambridge Books, Cambridge University Press, number 9780521824019, December.
    14. Ai, Chunrong & Norton, Edward C., 2003. "Interaction terms in logit and probit models," Economics Letters, Elsevier, vol. 80(1), pages 123-129, July.
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    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Twitter Adoption in Congress: Who Tweets First?
      by Ariel Goldring in Free Market Mojo on 2010-06-25 14:11:09

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    Cited by:

    1. 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.
    2. Hong, Sounman, 2012. "Online news on Twitter: Newspapers’ social media adoption and their online readership," Information Economics and Policy, Elsevier, vol. 24(1), pages 69-74.
    3. 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.

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    More about this item

    Keywords

    Communication; diffusion of technology; political marketing; social interaction; social media; social learning.;

    JEL classification:

    • M3 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation

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