The Genealogy of Ideology: Predicting Agreement and Persuasive Memes in the U.S. Courts of Appeals

Proceedings of the ACM Conference on AI and the Law, 2017

6 Pages Posted: 1 Aug 2016 Last revised: 20 Apr 2020

See all articles by Shivam Verma

Shivam Verma

New York University (NYU) - Courant Institute of Mathematical Sciences

Adithya Parthasarathy

New York University (NYU) - Courant Institute of Mathematical Sciences

Daniel L. Chen

Directeur de Recherche, Centre National de la Recherche Scientifique, Toulouse School of Economics, Institute for Advanced Study in Toulouse, University of Toulouse Capitole, Toulouse, France

Date Written: July 31, 2016

Abstract

We employ machine learning techniques to identify common characteristics and features from cases in the US courts of appeals that contribute in determining dissent. Our models were able to predict vote alignment with an average F1 score of 73%, and our results show that the length of the opinion, the number of citations in the opinion, and voting valence, are all key factors in determining dissent. These results indicate that certain high level characteristics of a case can be used to predict dissent. We also explore the influence of dissent using seating patterns of judges, and our results show that raw counts of how often two judges sit together plays a role in dissent. In addition to the dissents, we analyze the notion of memetic phrases occurring in opinions - phrases that see a small spark of popularity but eventually die out in usage - and try to correlate them to dissent.

Keywords: vote alignment, dissent, memes

Suggested Citation

Verma, Shivam and Parthasarathy, Adithya and Chen, Daniel L., The Genealogy of Ideology: Predicting Agreement and Persuasive Memes in the U.S. Courts of Appeals (July 31, 2016). Proceedings of the ACM Conference on AI and the Law, 2017, Available at SSRN: https://ssrn.com/abstract=2816707 or http://dx.doi.org/10.2139/ssrn.2816707

Shivam Verma

New York University (NYU) - Courant Institute of Mathematical Sciences ( email )

251 Mercer Street
New York, NY - 10012
United States

Adithya Parthasarathy (Contact Author)

New York University (NYU) - Courant Institute of Mathematical Sciences ( email )

251 Mercer Street
New York, NY - 10012
United States

Daniel L. Chen

Directeur de Recherche, Centre National de la Recherche Scientifique, Toulouse School of Economics, Institute for Advanced Study in Toulouse, University of Toulouse Capitole, Toulouse, France ( email )

Toulouse School of Economics
1, Esplanade de l'Université
Toulouse, 31080
France

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