MPEDS - Automated Coding of Protest Event Data

Machine-Learning Protest Event Data System

Quick Start
Annotation Interface

Automated Coding of Protest Event Data

The goal of the Machine-Learning Protest Event Data System (MPEDS) is to replace the labor-intensive process of having human coders look for information about protests in news sources with a computer-aided set of protocols.

Many researchers want to be able to study the conditions under which protest emerges or grows, the ways in which protest affects social policy, or the way protest is repressed. Researchers want to study whether movements around different types of issues exhibit different patterns and whether different “types” of places give rise to different movements. Do environmental movements have different patterns of growth from feminist or Black movements? Do they use similar or different tactics? Are they responded to similarly or differently by police? Human coding of news sources to find information about protests takes a long time and costs a lot in the wages for the human coders. For this reason, most projects are limited to a single issue or a short time period or just one or two news sources. These practical limits make it difficult to know how general the results from one study will be for other issues or places or time periods.

MPEDS is the first of its kind coming from within the social movement community that is specifically focused on identifying and coding information about protests. MPEDS uses recent innovations from machine learning and natural language processing to generate protest event data with little to no human intervention. This permits the timely coding of information about recent and current events and improves the ability to code information on historical events from the growing pool of sources that are available in machine-readable format.

Our first project that uses MPEDS identifies Black protests in news wire sources. Our application of the system in this project has been a hybrid approach, using system components (the haystack task) to help to identify these events. As the project matures, we hope to use more and more automated components to make this task easier for researchers. We will be using these data in a study of the correlates of Black protest over time and space.

Future projects using MPEDS include looking into the diffusion of anti-racism campus protest and the interplay of protest and social media in Black Lives Matter and indigenous protest in Canada.

As the project develops and grows, we will be posting new technical reports and published papers to this site.

Team members

Past team members


MPEDS development has been funded by National Science Foundation grant #1423784 and the Research and Scholar Activity Fund at University of Toronto Mississauga.