The field of political discourse analysis is moving towards a more nuanced understanding of how language is used to shape public perception and advance partisan narratives. Recent studies have highlighted the importance of analyzing question-answering strategies in political interviews and hearings, as well as the role of outliers in topic modeling as weak signals of emerging topics. The development of novel datasets and frameworks for analyzing media bias and political content in large language models has also been a key area of innovation. Noteworthy papers in this area include: C-QUERI, which develops a pipeline to extract question-answer pairs from unstructured hearing transcripts and constructs a novel dataset of committee hearings, revealing systematic differences in questioning strategies across parties. The Media Bias Detector, which introduces a large, ongoing dataset and computational framework for enabling systematic study of selection and framing bias in news coverage, providing a reusable methodology for studying media bias at scale.