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.
Anthology ID:
2022.acl-long.22
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
270–284
Language:
URL:
https://aclanthology.org/2022.acl-long.22
DOI:
10.18653/v1/2022.acl-long.22
Bibkey:
Cite (ACL):
Maryam Davoodi, Eric Waltenburg, and Dan Goldwasser. 2022. Modeling U.S. State-Level Policies by Extracting Winners and Losers from Legislative Texts. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 270–284, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Modeling U.S. State-Level Policies by Extracting Winners and Losers from Legislative Texts (Davoodi et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.22.pdf