Article Title
A Computational Analysis of Constitutional Polarization
Keywords
Textual analysis, Constitutional discourse, Political polarization
Abstract
This Article is the first to use computational methods to investigate the ideological and partisan structure of constitutional discourse outside the courts. We apply a range of machine- learning and text-analysis techniques to a newly available data set comprising all remarks made on the U.S. House and Senate floors from 1873 to 2016, as well as a collection of more recent newspaper editorials. Among other findings, we demonstrate (1) that constitutional discourse has grown increasingly polarized over the past four decades; (2) that polarization has grown faster in constitutional discourse than in nonconstitutlonal discourse; (3) that conservative-leaning speakers have driven this trend, (4) that members of Congress whose political party does not control the presidency or their own chamber are significantly more likely to invoke the Constitution in some, but not all, contexts; and (5) that contemporary conservative legislators have developed an especially coherent constitutional vocabulary, with which they have come to "own" not only terms associated with the document's original meaning but also terms associated with textual provisions such as the First Amendment. Above and beyond these concrete contributions, this Article demonstrates the potential for computational methods to advance the study of constitutional history, politics, and culture.
Recommended Citation
David E. Pozen, Eric L. Talley, and Julian Nyarko, A Computational Analysis of Constitutional Polarization, 105 Cornell L. Rev. 1
(2020)
Available at: https://scholarship.law.cornell.edu/clr/vol105/iss1/2