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

2022/05/19 by Russo, Giuseppe, Gote, Christoph, Brandenberger, Laurence +2
#Computation and Language (cs.CL) #Computers and Society (cs.CY) #Data Analysis #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Statistics and Probability (physics.data-an)

paper · doi:10.48550/arxiv.2205.09674

Abstract

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.

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