@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://preview.aclanthology.org/fix-sig-urls/2022.acl-long.22/",
    doi = "10.18653/v1/2022.acl-long.22",
    pages = "270--284",
    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."
}