Modeling U.S. State-Level Policies by Extracting Winners and Losers from Legislative Texts
Maryam Davoodi, Eric Waltenburg, Dan Goldwasser
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 data-driven 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 graph-based 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.
BibTeX
@inproceedings{davoodi-etal-2022-modeling,
title = "{M}odeling {U.S.} State-Level Policies by Extracting Winners and Losers from Legislative Texts",
author = "Davoodi, Maryam and
Waltenburg, Eric and
Goldwasser, Dan",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.22/",
doi = "10.18653/v1/2022.acl-long.22",
pages = "270--284"
}