<p dir="ltr">State-level policy decisions significantly influence various aspects of our daily lives, such as access to healthcare and education. Despite their importance, there is a limited understanding of how these policies and decisions are formulated within the legislative process. This dissertation aims to bridge that gap by utilizing data-driven methods and the latest advancements in machine learning (ML) and natural language processing (NLP). By leveraging data-driven approaches, we can achieve a more objective and comprehensive understanding of policy formation. The incorporation of ML and NLP techniques aids in processing and interpreting large volumes of complex legislative texts, uncovering patterns and insights that might be overlooked through manual analysis. In this dissertation, we pose new analytical questions about the state legislative process and address them in three stages:</p><p><br></p><p dir="ltr">First, we aim to understand the language of political agreement and disagreement in legislative texts. We introduce a novel NLP/ML task: predicting significant conflicts among legislators and sharp divisions in their votes on state bills, influenced by factors such as gender, rural-urban divides, and ideological differences. To achieve this, we construct a comprehensive dataset from multiple sources, linking state bills with legislators’ information, geographical data about their districts, and details about donations and donors. We then develop a shared relational and textual deep learning model that captures the interactions between the bill’s text and the legislative context in which it is presented. Our experiments demonstrate that incorporating this context enhances prediction accuracy compared to strong text-based models.</p><p><br></p><p dir="ltr">Second, we analyze the impact of legislation on relevant stakeholders, such as teachers in education bills. We introduce this as a new prediction task within our framework to better understand the state legislative process. To address this task, we enhance our modeling and expand our dataset using various techniques, including crowd-sourcing, to generate labeled data. This approach also helps us decode legislators’ decision-making processes and voting patterns. Consequently, we refine our model to predict the winners and losers of bills, using this information to more accurately forecast the legislative body’s vote breakdown based on demographic and ideological criteria.</p><p><br></p><p dir="ltr">Third, we enhance our analysis and modeling of state-level bills and policies using two techniques: We normalize the inconsistent, verbose, and complex language of state policies by leveraging Generative Large Language Models (LLMs). Additionally, we evaluate the policies within a broader network context by expanding the number of US states analyzed from 3 to 50 and incorporating new data sources, such as interest groups’ ratings of legislators and public information on legislators’ positions on various issues.</p><p><br></p><p dir="ltr">By following these steps in this dissertation, we aim to better understand the legislative processes that shape state-level policies and their far-reaching effects on society.</p>