MPEDS is a tool for facilitating the creation of protest event data. MPEDS uses recent innovations from machine learning and natural language processing to generate protest event data with little to no human intervention. The system aims to have the effect of increasing the speed and reducing the labor costs associated with identifying and coding collective action events in news sources, thus increasing the timeliness of protest data and reducing biases due to excessive reliance on too few news sources.

You will need to install Git LFS to properly download the large classifier and vectorizer files from this repository.

1. Hanna, Alex. 2017. MPEDS: Automating the Generation of Protest Event Data. SocArXiv. DOI: 10.31235/osf.io/xuqmv.
2. Oliver, Pamela, Chaeyoon Lim, Morgan Matthews and Alex Hanna. 2022. "Black Protests in the United States, 1994-2010." Sociological Science 9(May):275-312. DOI: 10.15195/v9.a12.
3. Oliver, Pamela, Alex Hanna, Chaeyoon Lim. 2022. “Constructing Relational and Verifiable Protest Event Data: Four Challenges and Some Solutions” Forthcoming in Mobilization. Preprint available https://osf.io/preprints/socarxiv/d89g7/

The DOI for this repository has been created with Zenodo.

You can cite this software as:

Hanna, Alex. 2017. MPEDS: Machine-Learning Protest Event Data System (v1.0). Zenodo. https://doi.org/10.5281/zenodo.886459

Development of this software has been supported by a National Science Foundation Graduate Research Fellowship and National Science Foundation grant SES-1423784. Thanks to Emanuel Ubert and Katie Fallon for working with this system since its inception, and to many undergraduate annotators who have put a lot of time working with and refining this system.