Disentangling Active and Passive Cosponsorship in the U.S. Congress

Autor:	Russo, Giuseppe, Gote, Christoph, Brandenberger, Laurence, Schlosser, Sophia, Schweitzer, Frank
Rok vydání:	2022
Předmět:	Computer Science - Machine Learning Computer Science - Computation and Language Computer Science - Computers and Society Physics - Data Analysis Statistics and Probability Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	In the U.S. Congress, legislators can use active and passive cosponsorship to support bills. We show that these two types of cosponsorship are driven by two different motivations: the backing of political colleagues and the backing of the bill's content. To this end, we develop an Encoder+RGCN based model that learns legislator representations from bill texts and speech transcripts. These representations predict active and passive cosponsorship with an F1-score of 0.88. Applying our representations to predict voting decisions, we show that they are interpretable and generalize to unseen tasks. Comment: 20 pages, 10 figures, 6 tables
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2205.09674 Zobrazit plný text záznamu View this record from Arxiv