Using Recurrent Neural Networks to Visualize Political Bias in News Media

Abstract

The growing polarization between Liberals and Conservatives in the United States today is largely caused and perpetuated by biased news sources. This thesis aims to begin restoring healthy political discourse and unifying young Americans by exposing this bias and enabling transparency on controversial issues. Taking inspiration from the work of Iyyer et al., we first leverage Long Short-Term Memory Networks, a type of Recurrent Neural Network, to generate a bias detection model that can, with significant accuracy, predict the liberal or conservative bias skews of a given sentence. By then applying that model to a sophisticated three-step analysis system, we can extract news article biases at the sentence-level, article-level, and topic-level. Finally, we can create simplistic and informative visualizations from that analysis to empower users with the ability to better understand the comparative ideological biases that are present in the news articles they consume.