political science | Andy Halterman

political science

Latent Civil War: Improving Inference and Forecasting with a Civil War Measurement Model

A new Bayesian measurement model for civil war and improved forecasting techniques.

Simple political actor classification with "soft" dictionaries

As political scientists, we are often interested in using text to understand the actions of political actors. Thankfully, have a growing set of tools for identifying political actors in text, including named entity recognition and dependency parses, custom event models, or hand labeling events text.

Introducing Mordecai 3: A Neural Geoparser and Event Geocoder in Python

Researchers working with text data are often faced with the problem of identifying place names in text and linking them to their geographic coordinates. In social science, we might want to measure news coverage of specific locations, track discussions of specific places in government documents, or geolocate events such protests to the locations where they occur.

Creating Custom Event Data Without Dictionaries: A Bag-of-Tricks

A bag of tricks for efficient, custom event data production, using transformer-based classifiers, question-answering models, Wikipedia entity linking, and active learning.

PLOVER and POLECAT: A New Political Event Ontology and Dataset

POLECAT is a new global event dataset for social science research, coded in the PLOVER event ontology.

Synthetically generated text for supervised text analysis

Synthetically generated text can help researchers address common issues in supervised text analysis.

Event Data in 30 Lines of Python

Much of my work involves improving large-scale systems to extract political events from text (see code from our NSF project on the subject here). These systems are designed for full production use over many hundreds of sources both daily and for the past in many dozens of event categories, including protests, armed conflict, statements, arrests, and humanitarian aid.

Managing Machine Learning Experiments

Reproducible methods like knitr and version control using git are on their way toward being standard for academic code, even in social science disciplines such as political science. knitr, Rmarkdown, and Jupyter notebooks make it easy to verify that your findings and figures come from the most recent version of your code and that it runs without errors.

Making Event Data From Scratch: A Step-By-Step Guide

This tutorial covers how to create event data from a new set of text using existing Open Event Data Alliance tools. After going through it, you should be able to use the OEDA event data pipeline for your own projects with your own text.