Abstract

The goal of this research is to leverage data science tools to the challenge of measuring narratives in legal and political texts, and then to analyze the causes and consequences of those narratives in the political economy. This proposal outlines a system for taking the plain text of documents, extracting the latent narrative structures in the text, and extracting the narratives that are causally related to some cause or outcome. The system will be applied to an analysis of prejudicial narratives in law, politics, and mass media. A data-driven approach that can extract text-based measures of narratives would mark a major step forward in this field. Many phenomena of interest to historians and social scientists are embedded in text, so the development and application of natural language processing methods to the analysis of narratives will be useful to researchers in a variety of fields and settings. In more specific reference to SDSC goals, this research seeks to make progress on an interdisciplinary data science problem (measuring narratives and analyzing associated causal links) and then focus on applications in a social-science domain (political economy of law and mass media). The deep learning models described below require significant computational infrastructure and expertise. To accelerate the empirical analysis by domain social scientists, we seek support from SDSC in data storage and management, implementation of statistical and machine learning models, and the development of compelling data visualizations. Our team share a core belief in reproducibility and open science initiatives.