As a scientist who works with computational methods, I am invested in understanding how to use computation to understand sociopolitical systems. My past work has included using natural language processing techniques to understand how politicians discuss policy issues on social media, and using API-based queries to understand social constructions of "dance" around the world.
MODELING POLITICAL ATTENTION
This code produces the unsupervised models and supervised classifiers that Libby Hemphill and I described in our ICWSM 2020 paper:
Hemphill, L., & Schöpke-Gonzalez, A. M. (2020). Two Computational Models for Analyzing Political Attention in Social Media. Proceedings of the AAAI Conference on Web and Social Media, 14(1), 260–271. https://www.aaai.org/ojs/index.php/ICWSM/article/view/7297.
Program code written in Python. Code available via: https://github.com/casmlab/modeling-political-attention
This program builds on the "Global Dance Culture Map" projects, and renders a map of photos of dance at sites around the world that people have shared on Flickr. Source code available at:
Website accessible at: https://agrgn42.github.io/539_Portfolio/index.html
Program code written in Python, PHP, HTML, & CSS. Code available via: https://github.com/agrgn42/dancemap
GLOBAL DANCE CULTURE MAP
This program produces a map in CartoDB that allows you to see what people are “saying” (in photos from Flickr) about dance and most frequently mentioned non-United States country that the New York Times has recently published about in dance-related article abstracts.
Program code written in Python & SQL. Code available via: https://github.com/agrgn42/global-dance-culture