NLP

Narratives in Law and Politics: A Computational Linguistics Approach

Started
January 1, 2021
Status
In Progress
Share this post

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.

People

Scientists

SDSC Team:
PI | Partners

description

Problem:

  • This project aims at leveraging data science approaches to (i) extract narratives in legal and political texts, and (ii) analyze the causes and consequences of those narratives in political economy. Such objectives require application and adoptation of natural language processing tools as well as data-driven techniques for causal discovery and inference in high dimensional data.

Impact:

  • The findings – methods as well as estimates of the real-world impacts of narratives in the political economy – will be useful to social scientists and policymakers interested in the political and cultural factors underlying social disadvantage. Additionaly, working with text data introduces a challenge, leading to new research question to be tackled with inovative machine learning tools.

Gallery

Annexe

Additionnal resources

Bibliography

  1. Angrist, J. and Pischke, J.-S. (2009). Mostly Harmless Econometrics: An empiricist’s companion. Princeton University Press, Princeton, NJ.
  2. Ash, E., Chen, D. L., and Naidu, S. (2017a). Ideas have consequences: The impact of law and economics on American justice. Technical report, NBER.
  3. Egami, N., Fong, C. J., Grimmer, J., Roberts, M. E., and Stewart, B. M. (2017). How to make causal inferences using texts.
  4. Galletta, S., Ash, E., and Chen, D. L. (2019). Do judicial sentiments affect social attitudes?
  5. Shiller, R. J. (2017). Narrative economics. American Economic Review, 107(4):967–1004.

Publications

Related Pages

More projects

ML4FCC

In Progress
Machine Learning for the Future Circular Collider Design
Big Science Data

CLIMIS4AVAL

In Progress
Real-time cleansing of snow and weather data for operational avalanche forecasting
Energy, Climate & Environment

SEMIRAMIS

Completed
AI-augmented architectural design
Energy, Climate & Environment

4D-Brains

In Progress
Extracting activity from large 4D whole-brain image datasets
Biomedical Data Science

News

Latest news

Climate-smart agriculture in sub-Saharan Africa: optimizing nitrogen fertilization with data science
November 6, 2023

Climate-smart agriculture in sub-Saharan Africa: optimizing nitrogen fertilization with data science

Climate-smart agriculture in sub-Saharan Africa: optimizing nitrogen fertilization with data science

Food insecurity in sub-Saharan Africa is widespread, with crop yields much lower than in many developed regions. The project aims to use laser spectroscopy to measure fluxes and isotopic composition of N2O from maize and potato crops subjected to a range of fertilization levels.
Street2Vec | Self-supervised learning unveils change in urban housing from street-level images
October 31, 2023

Street2Vec | Self-supervised learning unveils change in urban housing from street-level images

Street2Vec | Self-supervised learning unveils change in urban housing from street-level images

It is difficult to effectively monitor and track progress in urban housing. We attempt to overcome these limitations by utilizing self-supervised learning with over 15 million street-level images taken between 2008 and 2021 to measure change in London.
DLBIRHOUI | Deep Learning Based Image Reconstruction for Hybrid Optoacoustic and Ultrasound Imaging
February 28, 2023

DLBIRHOUI | Deep Learning Based Image Reconstruction for Hybrid Optoacoustic and Ultrasound Imaging

DLBIRHOUI | Deep Learning Based Image Reconstruction for Hybrid Optoacoustic and Ultrasound Imaging

Optoacoustic imaging is a new, real-time feedback and non-invasive imaging tool with increasing application in clinical and pre-clinical settings. The DLBIRHOUI project tackles some of the major challenges in optoacoustic imaging to facilitate faster adoption of this technology for clinical use.

Contact us

Let’s talk Data Science

Do you need our services or expertise?
Contact us for your next Data Science project!