BIDS Director Saul Perlmutter and BIDS Alumni Nick Adams co-lead this project (#4), which is being offered through UC Berkeley's Undergraduate Research Apprentice Program (URAP) for the Fall 2021 semester.
Public Editor is a collaborative news assessment platform that brings the public together to improve their own media literacy, evaluate the quality of information circulating on the Internet, and share their results with the broader public. Participating students will get first-hand experience building, refining, and launching a national-scale data science project that aims to engage thousands of public volunteers and news readers.
Students on this project will analyze data from Public Editor. Working alongside a national coalition of social science researchers and journalists, a Nobel Laureate, cognitive scientists, and software designers/developers, students will test the robustness of the Public Editor system, fortify it against attacks by trolls, and implement gamification features to ensure volunteers enjoy their experience. Students will create (1) a Jupyter notebook performing validation studies on the system to ensure the accuracy and reliability of users' labels; (2) Red Team scenarios and solutions to those scenarios, and (3) a codebase for automatically updating users' badges, points, and leaderboard status based upon their activity data.
You can also apply to join the sister project DemoWatch, a project identifying common sequences of interaction between protesters and governments and key decision points that result in violence, peace, and everything in between. 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.