Developing a Natural Language Understanding Model to Characterize Cable News Bias

18 May 2024

This paper is available on Arxiv under CC 4.0 license.


(1) Seth P. Benson, Carnegie Mellon University (e-mail:;

(2) Iain J. Cruickshank, United States Military Academy (e-mail:

Abstract and Intro

Related Research




Conclusion and References


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.

INDEX TERMS Natural Language Understanding, Cable News, Media Bias, Stance Analysis, Named Entity Recognition


The increasing trend of political polarization in the United States has garnered significant attention in recent literature. This trend, prominent at both national and local government levels, is reflected in media consumption patterns that indicate partisan polarization in the public [1].

Despite the plethora of studies on the subject, there is a noticeable gap in the literature regarding data-driven, computational analysis of the language used in political media sources.

This void is particularly conspicuous in the study of cable news, which has been linked to the intensification of polarization. Furthermore, although computational research extensively explores differing sentiments towards issues, the application of crosssubject bias models remains limited.

In this paper, we introduce an application of advanced Natural Language Understanding techniques to quantify the partisan bias in cable news. Bias is delineated by two principal aspects: what a source selects to discuss, and how they choose to portray it. By leveraging named entity recognition, we identify critical topic words within transcripts, followed by stance analysis to ascertain the positive or negative framing of these topics.

This approach facilitates an understanding of the diverse stances cable news programs adopt towards a set of topics, and how these programs differ in their choice of topics. Utilizing this information, we generate bias clusters of programs and examine their evolution over time. Our findings predominantly reveal consistent bias clusters strongly associated with a program’s network.

The methodology outlined in this paper provides a more adaptable approach to characterizing bias compared to previous studies in the field. Notably, our technique does not necessitate controlling for the topic discussed in the analyzed text, enabling its broad application to various political content. The primary contributions of our research include:

• We have developed a novel technique for characterizing bias in cable news. This technique combines named entity recognition and stance analysis, providing a comprehensive view of both the subjects covered by a program and the stance taken on those subjects.

• We have examined the consistency of program biases throughout 2020, providing a temporal analysis of bias in cable news. This analysis reveals how bias clusters evolve over time and how they are associated with the network of the program.

• We have demonstrated the superiority of stance detection over sentiment analysis in determining bias. Our findings show that stance detection, which considers both the subject and the perspective taken on the subject, provides a more nuanced and accurate measure of bias compared to sentiment analysis.

This paper is available on Arxiv under CC 4.0 license.