The field of deliberative discourse analysis is moving towards a deeper understanding of how opinions are shaped and shifted through computational models. Recent research has focused on developing innovative methods to analyze and predict opinion shifts, with applications in public policy-making, debate evaluation, and social media opinion mining. The integration of artificial intelligence, particularly large language models, has introduced new possibilities for examining political biases and alignments. Studies have shown that these models can reflect and amplify existing socio-political assumptions, highlighting the need for a more nuanced understanding of the complex interactions between technology, politics, and society. Noteworthy papers include: Capturing Opinion Shifts in Deliberative Discourse through Frequency-based Quantum deep learning methods, which presents a comparative analysis of NLP techniques for evaluating deliberative discourse. Artificial Authority: From Machine Minds to Political Alignments, which analyzes the democratic and autocratic biases in large-language models. Measuring Algorithmic Partisanship via Zero-Shot Classification and Its Implications on Political Discourse, which employs a zero-shot classification approach to evaluate algorithmic political partisanship.