Helping a Friend or Supporting a Cause? Disentangling Active and Passive Cosponsorship in the U.S. Congress
Giuseppe Russo, Christoph Gote, Laurence Brandenberger, Sophia Schlosser and Frank Schweitzer
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (2023)
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.
