Student Research from Data Driven Societies (Spring 2014)

In the spring of 2014, I (Jen Jack Gieseking) taught Data Driven Societies with Eric Gaze. A geographer and a mathematician, a social scientist and a natural scientist, working together with 35 students with very diverse backgrounds and interests sought to answer one question: what can data visualization reveal and obscure about the world’s increasing obsession with all things data?

Students selected a social justice hashtag of their choice that related to issues of identity, privacy, economics, politics, or the environment. Over a month, students scraped Twitter data on their hashtag. A hashtag is a term with a # in front of it that hyperlinks to all uses of the term that can range from #stopandfrisk and #smog to #gobears. As students read media and conducted research about the issue they had chosen to study, they also began to create graphs, maps, and network analyses from the Twitter data as well as a related dataset they had to find and bring to class. Students left the class with not only a basic understanding of software such as Excel, R, Social Explorer, CartoDB, and Gephi, but also a much more critical eye on the procurement, organization, and manipulation of data.

The outcomes were impressive and inspiring. Many of the students agreed to share their papers and/or presentations publicly, all of which are listed below or you can scroll through them at your leisure. Besides the work by students below, we share our course description as well. We hope you enjoy them as much as we did making them!

Links to student work:

Course description:

Big data and computational methods, such as changes in social media privacy laws and advances in mapping and network analysis, are changing financial markets, political campaigning, and higher education and becoming commonplace in our lives. Our daily existence is increasingly structured by code, from the algorithms that time our traffic lights to those that filter our search criteria and record our thoughts and ideas. In this course, we explore the possibilities, limitations, and implications of using digital and computational methods and analytics to study issues that affect our everyday lives from a social scientific approach. We pay special attention to the ways we collect, trust, analyze, portray, and use data, most especially the tools and meanings involved in data visualization and modeling.

This course tackles a number of cutting-edge issues and questions that confront society today: What sorts of questions can be asked and answered using digital and computational methods to rethink our relationships to data and what can data can show us about the world? How do we construct models to help us better understand social phenomena and associated data? What is data, and how do we know it’s reliable? How do these methods complement and sometimes challenge traditional methodologies in the social sciences? Students will leave the course with both substantive experience in digital and computational methods, Students will learn how to apply a critical lens for understanding and evaluating what computers can (and cannot) bring to the study of society.