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        <title>Modeling U.S. State-Level Policies by Extracting Winners and Losers from Legislative Texts</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T Modeling U.S. State-Level Policies by Extracting Winners and Losers from Legislative Texts
%A Davoodi, Maryam
%A Waltenburg, Eric
%A Goldwasser, Dan
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F davoodi-etal-2022-modeling
%X 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.
%R 10.18653/v1/2022.acl-long.22
%U https://aclanthology.org/2022.acl-long.22/
%U https://doi.org/10.18653/v1/2022.acl-long.22
%P 270-284

Markdown (Informal)

[Modeling U.S. State-Level Policies by Extracting Winners and Losers from Legislative Texts](https://aclanthology.org/2022.acl-long.22/) (Davoodi et al., ACL 2022)

• Modeling U.S. State-Level Policies by Extracting Winners and Losers from Legislative Texts (Davoodi et al., ACL 2022)

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.