Capturing Political Polarization of Reddit Submissions in the Trump Era

The American political situation of the last years, combined with the incredible growth of Social Networks, led to the diffusion of political polarization's phenomenon online. Our work presents a model that attempts to measure the political polarization of Reddit submissions during the first half of Donald Trump's presidency. To do so, we design a text classification task: Political polarization of submissions is assessed by quantifying those who align themselves with pro-Trump ideologies and vice versa. We build our ground truth by picking submissions from subreddits known to be strongly polarized. Then, for model selection, we use a Neural Network with word embeddings and Long Short Time Memory layer and, finally, we analyze how model performances change trying different hyper-parameters and types of embeddings.