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
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.acl-long.22.pdf