Predicting Political Leanings With Machine Learning

This study proposes an alternative approach to predicting political sentiment in the United States by employing machine learning models. Traditional methods of predicting political sentiment have become outdated and require reevaluation. This work utilizes a variety of features, including Google Trends data and Census demographics, to create ensemble models capable of providing more accurate and insightful predictions of political leanings and electoral outcomes. By leveraging the power of machine learning, this research represents a significant innovation over traditional polling and prediction models. The resulting models achieve high accuracy in both training and validation sets, enabling a deeper understanding of political leanings and insights into their dynamics.</p

Beating Trump as "Job One": Media Framing of Electability in the 2020 Democratic Presidential Primary

The 2020 Democratic Primary field was the most diverse in history but narrowed to two septuagenarian white men, Joe Biden and Bernie Sanders, with the former winning the primary. Many candidates of color and women candidates left the race before voting began; consequently, many voters were not able to vote for a candidate who was not a white man. “Electability,” a state in which a candidate is perceived to have qualities that make success in a general election likely, frequently arose in media discussions of the candidates. This thesis examines the media frames surrounding electability by analyzing the myths that explain Hillary Clinton’s 2016 loss, which elevate different demographics as important for Democrats to win over for success in 2020. It then investigates how their concerns inform two contradictory prototypes for what an “electable” candidate looks like and the impact these prototypes have had on women and minority candidates

Towards a New Ontology of Polling Inaccuracy: The Benefits of Conceiving of Elections as Heterogenous Phenomena for the Study of Pre-election Polling Error

A puzzle exists at the heart of pre-election polling. Despite continual methodological improvement and repeated attempts to identify and correct issues laid bare by misprediction, average polling accuracy has not notably improved since the conclusion of the Second World War. In this thesis, I contend that this is the result of a poll-level focus within the study of polling error that is both incommensurate with its evolution over time and the nature of the elections that polls seek to predict. I hold that differences between elections stand as a plausible source of polling error and situate them within a novel four-level model of sources of polling error. By establishing the heterogenous nature of elections as phenomena and its expected impact on polling error, I propose a new election-level ontology through which the inaccuracy of polls can be understood. I test the empirical validity of this new ontology by using a novel multi-level model to analyse error across the most expansive polling dataset assembled to date, encompassing 11,832 in-campaign polls conducted in 497 elections across 83 countries, finding that membership within different elections meaningfully impacts polling error variation. With the empirical validity of my proposed ontology established, I engage in an exploratory analysis of its benefits, finding electoral characteristics to be useful in the prediction of polling error. Ultimately, I conclude that the adoption of a new, multi-level ontology of polling error centred on the importance of electoral heterogeneity not only offers a more comprehensive theoretical account of its sources than current understandings, but is also more specifically tailored to the reality of pre-election polling than existing alternatives. I also contend that it offers pronounced practical benefits, illuminating those circumstances in which polling error is likely to vary