This project is being offered through UC Berkeley's Undergraduate Research Apprentice Program (URAP). For the Fall 2020 semester, this project is seeking 5 students with experience & interest in data modeling and supervised ML. Eligible undergraduates may apply online August 19-31, 2020.
The Demo Watch project has collected and is curating over 8,000 news articles describing all the interactions between police and protesters during the Occupy movement. This semester, students will work with senior researchers and professors from Goodly Labs, NYU, and the Univ. of Michigan to (1) implement/code a multi-level time-series model that will analyze curated Demo Watch data to find patterns of peaceful and violent activity; and (2) create a text classifier, via supervised machine learning, that is capable of scanning through news articles about protest to identify important data for analysis. Ideally, the semester will end with (1) a Jupyter notebook that intakes Demo Watch data and outputs data-enriched models of police/protester interaction and (2) a Jupyter notebook that intakes Demo Watch data and creates a text classifier via supervised ML.
Demo Watch invites the public to participate in the world’s largest and most sophisticated project researching the dynamics of political contestation and violence. With vast and intricate data, the project is identifying common sequences of interaction between protesters and governments and key decision points that result in violence, peace, and everything in between. Findings will be used to help prevent future political violence. This project is now operated in partnership with Goodly Labs, a non-profit organization founded by former BIDS Research Fellow Nick Adams. He and BIDS Director Saul Perlmutter co-lead this project.