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Measuring discourse by algorithm

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  • Caspi, Aviv
  • Stiglitz, Edward H.

Abstract

Scholars increasingly use machine learning techniques such as Latent Dirichlet Allocation (LDA) to reduce the dimensionality of textual data and to study discourse in collective bodies. However, measures of discourse based on algorithmic results typically have no intuitive meaning or obvious relationship to humanly observed discourse. Such measures of discourse must be carefully validated before relied on and interpreted. We examine several common measures of discourse based on algorithmic results, and propose a number of ways to validate them in the setting of Federal Open Market Committee meetings. We also suggest that validation techniques may be used as a principled approach to model selection and parameterization.

Suggested Citation

  • Caspi, Aviv & Stiglitz, Edward H., 2020. "Measuring discourse by algorithm," International Review of Law and Economics, Elsevier, vol. 62(C).
    • Handle: RePEc:eee:irlaec:v:62:y:2020:i:c:s0144818819302571
    DOI: 10.1016/j.irle.2019.105863
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    References listed on IDEAS

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    1. Grimmer, Justin & Stewart, Brandon M., 2013. "Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts," Political Analysis, Cambridge University Press, vol. 21(3), pages 267-297, July.
    2. Bauke Visser & Otto H. Swank, 2007. "On Committees of Experts," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 122(1), pages 337-372.
    3. Stephen Hansen & Michael McMahon & Andrea Prat, 2018. "Transparency and Deliberation Within the FOMC: A Computational Linguistics Approach," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(2), pages 801-870.
    4. Margaret E. Roberts & Brandon M. Stewart & Edoardo M. Airoldi, 2016. "A Model of Text for Experimentation in the Social Sciences," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 988-1003, July.
    5. Bengt Holmström, 1999. "Managerial Incentive Problems: A Dynamic Perspective," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 66(1), pages 169-182.
    6. Bengt Holmström, 1999. "Managerial Incentive Problems: A Dynamic Perspective," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 66(1), pages 169-182.
    7. Kevin M. Quinn & Burt L. Monroe & Michael Colaresi & Michael H. Crespin & Dragomir R. Radev, 2010. "How to Analyze Political Attention with Minimal Assumptions and Costs," American Journal of Political Science, John Wiley & Sons, vol. 54(1), pages 209-228, January.
    8. Parthasarathy, Ramya & Rao, Vijayendra & Palaniswamy, Nethra, 2019. "Deliberative Democracy in an Unequal World: A Text-As-Data Study of South India’s Village Assemblies," American Political Science Review, Cambridge University Press, vol. 113(3), pages 623-640, August.
    9. Ellen E. Meade, 2005. "The FOMC: preferences, voting, and consensus," Review, Federal Reserve Bank of St. Louis, vol. 87(Mar), pages 93-101.
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    Cited by:

    1. Chin, Jason & Zeiler, Kathryn, 2021. "Replicability in Empirical Legal Research," LawArXiv 2b5k4, Center for Open Science.

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