Helping a Friend or Supporting a Cause? Disentangling Active and Passive Cosponsorship in the U.S. Congress
Giuseppe Russo, Christoph Gote, Laurence Brandenberger, Sophia Johanna Schlosser, Frank Schweitzer
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Main: Computational Social Science and Cultural Analytics Main-poster Paper
Poster Session 4: Computational Social Science and Cultural Analytics (Poster)
Conference Room: Frontenac Ballroom and Queen's Quay
Conference Time: July 11, 11:00-12:30 (EDT) (America/Toronto)
Global Time: July 11, Poster Session 4 (15:00-16:30 UTC)
Keywords:
human behavior analysis
TLDR:
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...
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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.