Developing a Natural Language Understanding Model to Characterize Cable News Bias
This work addresses the issue of subjective human labeling in media bias research for cable news, offering a more objective tool for analysis.
The authors tackled the problem of characterizing cable news bias by developing an unsupervised machine learning method that uses Named Entity Recognition and Stance Analysis to cluster programs, finding that clusters are consistent over time and roughly correspond to the cable news network.
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.