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.

翻译：媒体偏见一直是社会科学和计算科学广泛研究的课题。然而，当前研究在标注偏见时仍高度依赖人工输入和主观评估，有线新闻研究领域尤为如此。为解决这些问题，我们提出了一种无监督机器学习方法，可在无需人工干预的情况下表征有线新闻节目的偏见。该方法通过命名实体识别分析提及的话题，并结合立场分析探讨这些话题的讨论方式，从而将具有相似偏见的节目聚类。我们将该方法应用于2020年有线新闻文本记录，发现节目聚类结果随时间保持一致，且大致对应于节目所属的有线新闻网络。该方法揭示了未来工具在客观评估媒体偏见及表征陌生媒体环境方面的潜力。