How Predictable is Your State? Leveraging Lexical and Contextual Information for Predicting Legislative Floor Action at the State Level

Vladimir Eidelman, Anastassia Kornilova, Daniel Argyle

Abstract
Modeling U.S. Congressional legislation and roll-call votes has received significant attention in previous literature, and while legislators across 50 state governments and D.C. propose over 100,000 bills each year, enacting over 30% of them on average, state level analysis has received relatively less attention due in part to the difficulty in obtaining the necessary data. Since each state legislature is guided by their own procedures, politics and issues, however, it is difficult to qualitatively asses the factors that affect the likelihood of a legislative initiative succeeding. We present several methods for modeling the likelihood of a bill receiving floor action across all 50 states and D.C. We utilize the lexical content of over 1 million bills, along with contextual legislature and legislator derived features to build our predictive models, allowing a comparison of what factors are important to the lawmaking process. Furthermore, we show that these signals hold complementary predictive power, together achieving an average improvement in accuracy of 18% over state specific baselines.
Anthology ID:
C18-1013
Volume:
Proceedings of the 27th International Conference on Computational Linguistics
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico, USA
Editors:
Emily M. Bender, Leon Derczynski, Pierre Isabelle
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
145–160
Language:
URL:
https://preview.aclanthology.org/add_missing_videos/C18-1013/
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Cite (ACL):
Vladimir Eidelman, Anastassia Kornilova, and Daniel Argyle. 2018. How Predictable is Your State? Leveraging Lexical and Contextual Information for Predicting Legislative Floor Action at the State Level. In Proceedings of the 27th International Conference on Computational Linguistics, pages 145–160, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
Cite (Informal):
How Predictable is Your State? Leveraging Lexical and Contextual Information for Predicting Legislative Floor Action at the State Level (Eidelman et al., COLING 2018)
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https://preview.aclanthology.org/add_missing_videos/C18-1013.pdf
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