Using Recurrent Neural Networks to Model Political Outcomes
School Name
South Carolina Governor's School for Science & Mathematics
Grade Level
12th Grade
Presentation Topic
Computer Science
Presentation Type
Mentored
Abstract
In an increasingly data-driven world, political scientists and statisticians are searching for new methods of predicting election outcomes. Traditionally, these forecasts have relied on polling as well as standard regression models. As machine learning becomes an increasingly robust field, it is natural to consider extending its applications to this area. Recurrent neural networks are an effective tool for time-series forecasting, and this study evaluated their applicability in political forecasting. Given datasets with and without 2012 election data, a recurrent neural network produced models that were, respectively, 97% and 92% accurate at the county level. The Keras and TensorFlow libraries were effective in facilitating a machine learning model for this purpose. Changing factors within the neural network such as the test set size, the number of hidden layers, and the optimizer had a measurable but not drastic effect on the overall accuracy. Demographic factors within the model that increased accuracy the most were the racial statistics, percent in professional occupations, and percent driving alone to work. The model was most accurate in Southern states but no other trends appeared, with North Carolina being the most accurate large state and Illinois the least. The accuracy of this model showcases that neural networks have promising applications in the political realm, and can be applied to future modeling at a sufficiently granular level.
Recommended Citation
Fulton, Ethan, "Using Recurrent Neural Networks to Model Political Outcomes" (2020). South Carolina Junior Academy of Science. 92.
https://scholarexchange.furman.edu/scjas/2020/all/92
Location
Furman Hall 109
Start Date
3-28-2020 10:15 AM
Presentation Format
Oral Only
Group Project
No
Using Recurrent Neural Networks to Model Political Outcomes
Furman Hall 109
In an increasingly data-driven world, political scientists and statisticians are searching for new methods of predicting election outcomes. Traditionally, these forecasts have relied on polling as well as standard regression models. As machine learning becomes an increasingly robust field, it is natural to consider extending its applications to this area. Recurrent neural networks are an effective tool for time-series forecasting, and this study evaluated their applicability in political forecasting. Given datasets with and without 2012 election data, a recurrent neural network produced models that were, respectively, 97% and 92% accurate at the county level. The Keras and TensorFlow libraries were effective in facilitating a machine learning model for this purpose. Changing factors within the neural network such as the test set size, the number of hidden layers, and the optimizer had a measurable but not drastic effect on the overall accuracy. Demographic factors within the model that increased accuracy the most were the racial statistics, percent in professional occupations, and percent driving alone to work. The model was most accurate in Southern states but no other trends appeared, with North Carolina being the most accurate large state and Illinois the least. The accuracy of this model showcases that neural networks have promising applications in the political realm, and can be applied to future modeling at a sufficiently granular level.
