Developing a Natural Language Understanding Model to Characterize Cable News Bias

TL;DR

This paper introduces an unsupervised machine learning approach to objectively characterize cable news bias by analyzing topics and discussion styles, avoiding subjective human labeling.

Contribution

The study presents a novel unsupervised method combining Named Entity Recognition and Stance Analysis to cluster cable news programs based on bias without human input.

Findings

Program clusters are consistent over time.

Clusters roughly align with cable news networks.

Method shows potential for objective media bias assessment.

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 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 program clusters are consistent over time and roughly correspond to the cable news network of the program. This method reveals the potential for future tools to objectively assess media bias and characterize unfamiliar media environments.