A Machine Learning Pipeline to Examine Political Bias with Congressional Speeches

Computational methods to model political bias in social media involve several challenges due to heterogeneity, high-dimensionality, multiple modalities, and the scale of the data. Most of the current political bias detection methods rely heavily on the manually-labeled ground-truth data for the underlying political bias prediction tasks. Such methods are human-intensive labeling, labels related to only a specific problem, and the inability to determine the near future bias state of a social media conversation. In this work, we address such problems and give machine learning approaches to study political bias in two ideologically diverse social media forums: Gab and Twitter without the availability of human-annotated data. We propose a method to exploit the features of entities on transcripts collected from political speeches in US congress to label political bias of social media posts automatically without any human intervention. With existing machine learning algorithms we achieve the highest accuracy of 70.5% and 65.1% to predict posts on Twitter and Gab data respectively. We also present a machine learning approach that combines features from cascades and text to forecast cascade’s political bias with an accuracy of about 85%.