Modeling U.S. State-Level Policies by Extracting Winners and Losers from Legislative Texts

Abstract

Decisions on state-level policies have a deep effect on many aspects of our everyday life, such as health-care and education access. However, there is little understanding of how these policies and decisions are being formed in the legislative process. We take a datadriven approach by decoding the impact of legislation on relevant stakeholders (e.g., teachers in education bills) to understand legislators' decision-making process and votes. We build a new dataset for multiple US states that interconnects multiple sources of data including bills, stakeholders, legislators, and money donors. Next, we develop a textual graphbased model to embed and analyze state bills. Our model predicts winners/losers of bills and then utilizes them to better determine the legislative body's vote breakdown according to demographic/ideological criteria, e.g., gender.

PAR: Political Actor Representation Learning with Social Context and Expert Knowledge

“…Political scientists first explored to quantitatively model political actors based on their voting behaviour. Ideal point model (Clinton et al, 2004) In addition to voting, textual data such as speeches and public statements are also leveraged to model political perspectives (Evans et al, 2007;Thomas et al, 2006;Hasan and Ng, 2013;Sinno et al, 2022;Liu et al, 2022;Davoodi et al, 2022;Alkiek et al, 2022;Dayanik et al, 2022;Pujari and Goldwasser, 2021;Villegas et al, 2021;Sen et al, 2020;Baly et al, 2020). Volkova et al (2014) leverage message streams to inference users political preference.…”