A Machine Learning Pipeline to Examine Political Bias with Congressional Speeches

Machine learning, with advancements in natural language processing and deep learning, has been actively used in studying political bias on social media. But the key challenge to model political bias is the requirement of human effort to label the seed social media posts to train machine learning models. Although very effective, this approach has disadvantages in the time-consuming data labeling process and the cost to label significant data for machine learning models is significantly higher. The web offers invaluable data on political bias starting from biased news media outlets publishing articles on socio-political issues to biased user discussions about several topics in multiple social forums. In this work, we introduce a novel approach to label political bias for social media posts directly from US congressional speeches without any human intervention for downstream machine learning models.