Dynamics of Political Polarization: Insights from Using Machine Learning and Natural Language…

The American public increasingly finds itself bitterly divided over political differences. Survey indicators, partisan media, and the public's voting patterns inform this sense of division in our politics. That said, we use applications of Machine Learning and Natural Language Processing (NLP) methods in a novel way to paint a more nuanced picture of divisions in American political opinions. It turns out that even very simple NLP methods that rely on simple word frequencies in politicians' tweets can be extremely predictive when it comes to predicting party affiliation, getting over 80% accuracy without any special tuning. These simple models are very robust: a model trained on the tweets from the House of the Representatives can be equally predictive when tested on the tweets from the US Senators.