Developing a Natural Language Understanding Model to Characterize Cable News Bias

Abstract

Media bias has been extensively studied by both social and computational sciences. However, current work still has a large reliance on human input and subjective assessment to label biases. This is especially true for cable news, which has a continued presence in American media but a lack of text-based bias identification in research. To address these issues, we develop an unsupervised machine learning method to characterize the bias of cable news programs without any human input. This method relies on the analysis of what topics are mentioned through Named Entity Recognition and how those topics are discussed through Stance Analysis in order to cluster programs with similar biases together. Applying our method to 2020 cable news transcripts, we find that cable news programs tend to cluster together consistently over time and roughly correspond to the cable news network of the program. This method reveals the potential for future tools to more objectively assess media bias and characterize unfamiliar media environments, and the empirical results insight into the nature of bias in American cable news programs.