By Natalie Appel ‘23, Noah Cohen ‘22, Spencer Dean ‘21, Sam Feuer ‘23, Magda Kisielinska ‘22, and Brianna Mebane ‘22
Since 2010, the Wesleyan Media Project has hand coded American political advertisements for an extensive list of variables relating to content and tone. The information collected through this process is insightful, however it is a time intensive task. Thus, the initial question we sought to answer was how much, if any, of this process could be automated in order to keep up with the scale of digital advertising. Our goal was to do so by training machine learning models on the text of the existing hand coded ads in order to predict the characteristics of new ads. We tested a number of methods and ultimately found that our Random Forest worked best for binary issue variables while a distilBERT Neural Network worked best for multi class variables, especially ad tone.