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

arXiv: https://arxiv.org/abs/2205.09674 · HTML: https://mlsanity.com/paper/2205.09674/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 56 references that are in the map.
- First posted on arXiv 19 May 2022.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Gender Bias in Contextualized Word Embeddings](https://mlsanity.com/paper/1904.03310/) (2019)
- [Distinguish Confusing Law Articles for Legal Judgment Prediction](https://mlsanity.com/paper/2004.02557/) (2020)
- [Text-Based Ideal Points](https://mlsanity.com/paper/2005.04232/) (2020)
- [Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text Generation](https://mlsanity.com/paper/2004.14983/) (2020)
- [What About the Precedent: An Information-Theoretic Analysis of Common Law](https://mlsanity.com/paper/2104.12133/) (2021)
- [Understanding Politics via Contextualized Discourse Processing](https://mlsanity.com/paper/2012.15784/) (2020)
- [Classifying Dyads for Militarized Conflict Analysis](https://mlsanity.com/paper/2109.12860/) (2021)
- [Party Matters: Enhancing Legislative Embeddings with Author Attributes for Vote Prediction](https://mlsanity.com/paper/1805.08182/) (2018)
- [How Predictable is Your State? Leveraging Lexical and Contextual Information for Predicting Legislative Floor Action at the State Level](https://mlsanity.com/paper/1806.05284/) (2018)

## Built on by (most cited first, 4 of 4)

- [Spillover of Antisocial Behavior from Fringe Platforms: The Unintended Consequences of Community Banning](https://mlsanity.com/paper/2209.09803/) (2022)
- [Understanding Online Migration Decisions Following the Banning of Radical Communities](https://mlsanity.com/paper/2212.04765/) (2022)
- [Extracting Victim Counts from Text](https://mlsanity.com/paper/2302.12367/) (2023)
- [UPB @ ACTI: Detecting Conspiracies using fine tuned Sentence Transformers](https://mlsanity.com/paper/2309.16275/) (2023)

## Abstract (arXiv)

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.