@inproceedings{helm-etal-2026-toward,
title = "Toward A Digital Twin of {U}.{S}. Congress",
author = "Helm, Hayden and
Chen, Tianyi and
McGuinness, Harvey and
Lee, Paige and
Duderstadt, Brandon and
Priebe, Carey",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.493/",
pages = "10148--10160",
ISBN = "979-8-89176-395-1",
abstract = "In this paper we provide evidence that our virtual model of U.S. congresspersons based on a collection of language models moves towards satisfying the definition of a digital twin. In particular, we introduce and provide high-level descriptions of a daily-updated dataset that contains every Tweet from every U.S. congressperson during their respective terms. We demonstrate that a modern language model equipped with congressperson-specific subsets of this data producing Tweets that are largely indistinguishable from actual Tweets posted by their physical counterparts. We illustrate how generated Tweets can be used to predict roll-call vote behaviors and to quantify the likelihood of congresspersons crossing party lines, thereby assisting stakeholders in allocating resources and potentially impacting real-world legislative dynamics. We conclude with a discussion of the limitations and important extensions of our analysis."
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%0 Conference Proceedings
%T Toward A Digital Twin of U.S. Congress
%A Helm, Hayden
%A Chen, Tianyi
%A McGuinness, Harvey
%A Lee, Paige
%A Duderstadt, Brandon
%A Priebe, Carey
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F helm-etal-2026-toward
%X In this paper we provide evidence that our virtual model of U.S. congresspersons based on a collection of language models moves towards satisfying the definition of a digital twin. In particular, we introduce and provide high-level descriptions of a daily-updated dataset that contains every Tweet from every U.S. congressperson during their respective terms. We demonstrate that a modern language model equipped with congressperson-specific subsets of this data producing Tweets that are largely indistinguishable from actual Tweets posted by their physical counterparts. We illustrate how generated Tweets can be used to predict roll-call vote behaviors and to quantify the likelihood of congresspersons crossing party lines, thereby assisting stakeholders in allocating resources and potentially impacting real-world legislative dynamics. We conclude with a discussion of the limitations and important extensions of our analysis.
%U https://aclanthology.org/2026.findings-acl.493/
%P 10148-10160
Markdown (Informal)
[Toward A Digital Twin of U.S. Congress](https://aclanthology.org/2026.findings-acl.493/) (Helm et al., Findings 2026)
ACL
- Hayden Helm, Tianyi Chen, Harvey McGuinness, Paige Lee, Brandon Duderstadt, and Carey Priebe. 2026. Toward A Digital Twin of U.S. Congress. In Findings of the Association for Computational Linguistics: ACL 2026, pages 10148–10160, San Diego, California, United States. Association for Computational Linguistics.