Alumni spotlight: Shea Minter applies machine learning methods to understand political extremism

Written by
Allison Gasparini
May 28, 2024

It wasn’t until Shea Minter was preparing for graduate school that she really started thinking about how her undergraduate certificate in Statistics and Machine Learning would be applicable to social science research.

Minter, class of 2019, received her bachelor’s degree in politics with a focus on international relations at Princeton University’s Department of Politics. At the time, she completed her requirements for a certificate (now a minor) from the Center for Statistics and Machine Learning. “They were kind of separate trajectories,” said Minter of when she first began pursuing SML alongside politics. 

Now a Ph.D. candidate in the Government Department at Georgetown University, Minter is using statistical methods to study right wing extremism – their political behavior and interaction with democracy. “My dissertation is specifically asking why members of this ideology run for office,” said Minter. 

It’s easy to track the seeds of Minter’s current work to her time at CSML. As a senior at Princeton, she conducted a research project focused centrally on understanding how racial attitudes impact foreign policy decisions.

For her work at Georgetown, Minter is focused mainly on historical cases involving political groups – such as the American Nazi party, individual actors – like David Duke, and violent extremist events – including the Oklahoma City bombing. Her data sources are archival records, ranging from FBI data to court case records to private writings. “I use the text as a quantitative data source, basically,” said Minter. 

An exploratory map of racist far right terrorists compiled by Minter during her research.

An exploratory map of racist far right terrorists compiled by Minter during her research. Courtesy of Shea Minter.

Her major finding so far is that when right-wing extremist actors run for office, they don’t often believe they’ll win. More likely than not, their central goal is to have a platform by which to recruit more people to their ideology. “The cool and sometimes bad thing about my topic is I do think a lot of it is somewhat common sense,” said Minter. “But it hasn’t been written yet even though it’s becoming a more and more relevant topic in the current political landscape.” 

Minter learned how to code in R during her time in the Department of Politics at Princeton and continues to use the language today for her statistical analyses. “I try to keep my statistics as simple as possible,” she said, citing her desire to keep her research understandable and easily communicated to any scholar. “My goal is to make the results as reproducible and straightforward as possible.”

Of her time as an undergraduate at CSML, Minter said it not only prepared her for graduate-level statistics – she also learned a scientific way of thinking that allows her to approach data sources both qualitative and quantitative in unique ways. “SML has really taught me a way of thinking that is more applicable beyond just the methods,” said Minter. 

Minter said she’d recommended the minor at CSML to students of all disciplines, not only because it was fun but because it could be useful to anyone, no matter their area of interest. “Statistics is everywhere,” said Minter. “You’re always going to need literacy in statistics data.”