Socially disruptive periods and topics from information-theoretical analysis of judicial decisions

Abstract

Laws and legal decision-making regulate how societies function. Therefore, they evolve and adapt to new social paradigms and reflect changes in culture and social norms, and are a good proxy for the evolution of socially sensitive issues. Here, we use an information-theoretic methodology to quantitatively track trends and shifts in the evolution of large corpora of judicial decisions, and thus to detect periods in which disruptive topics arise. When applied to a large database containing the full text of over 100,000 judicial decisions from Spanish courts, we are able to identify an abrupt change in housing-related decisions around 2016. Because our information-theoretic approach pinpoints the specific content that drives change, we are also able to interpret the results in terms of the role played by legislative changes, landmark decisions, and the influence of social movements.

Bayesian estimation of information-theoretic metrics for sparsely sampled distributions

“…In neuroscience, Shannon entropy of spike train distributions characterizes brain activity from neural responses [3], while mutual information identifies correlations between brain stimuli and responses [4]. Recently, the Kullback-Leibler divergence [5] and its regularized version, the Jensen-Shannon distance, have also been successfully used in a wide variety of contexts: in cognitive science as a measure of "surprise," to quantify and predict how human attention is oriented between changing screen images [6]; in quantitative social science, in combination with topic models, to track the propagation of political and social discourses [7,8] or to understand the emergence of social disruption from the analysis of judicial decisions [9]; and in machine learning, at the intersection between the statistical physics of diffusive processes, probabilistic models and deep neural networks [10].…”