The lobbying industry involves billions of dollars and plays a crucial role in American politics, yet existing datasets suffer from critical limitations that hinder robust analysis. The goal of my research is to address some of these challenges by developing methods to disambiguate and augment lobbyist data through computational techniques. This involves implementing algorithms that can identify duplicate lobbyist entries and enrich profiles with relevant information such as government employment history, political donations, and party affiliations. The enhanced dataset seeks to make the analysis of lobbying influence more accurate and reliable, as well as provide a clearer picture of the relationships between lobbyists, legislators, and policy outcomes, ultimately working toward a more transparent view of how corporate lobbying influences American governance.

Through SuperUROP, I’ll be able to learn what it’s like to go through the process of dedicated research and writing a full research paper. In previous UROPs, I’ve conducted research that served an important purpose, but taking that to the next level and presenting this research to others is what I hope to get in SuperUROP. I want to inform people about the elusive lobbying industry and gain crucial academic and research skills in the process.