Robert Sprague, University of Wyoming, College of Business, Kevin Grauberger, University of Wyoming, and Nicole Barberis, Bloomberg LP, have published One Hundred Twenty Years of U.S. Privacy Law Scholarship: A Latent Semantic Analysis. Here is the abstract.
This paper reports results from a research project aimed at identifying fundamental privacy law principles derived from the writings of legal scholars and commentators using probabilistic topic modeling, which is comprised of a suite of algorithms that attempt to discover hidden thematic structures in large archives of documents. Topic modeling algorithms are statistical methods that analyze the words of texts to discover topics (themes) contained within, how those topics are connected to each other, and how they change over time. A latent Dirichlet allocation process, which identifies sets of terms that more tightly co-occur, is incorporated into the topic modeling analysis to identify words most closely associated with each identified topic. The latent Dirichlet allocation therefore provides insight into the context in which each identified topic occurs. Our analysis reveals that privacy law in the United States comports most closely with the Georgia Supreme Court’s 1905 description of privacy from the seminal case Pavesich v. New England Life Insurance Company: “the right of a person to be secure from invasion by the [government or] public into matters of a private nature.”Download the paper from SSRN at the link.