Political Orientation

Political orientation research increasingly focuses on understanding how biases manifest in large language models (LLMs) and how these models can be used to analyze political sentiment in text data from various sources, including social media and news articles. Current research employs machine learning techniques, including supervised and semi-supervised learning, to predict political leaning from text, images, and even user network data, often comparing the performance of different LLMs and algorithms. These studies highlight the potential for algorithmic bias to influence information dissemination and public opinion, underscoring the need for more transparent and robust methods for analyzing political polarization and mitigating the impact of biased algorithms.