Align Voting Behavior with Public Statements for Legislator Representation Learning

Mou, Xin-Yi; Wei, Zhongyu; Chen, Lei; Ning, Shangyi; He, Yancheng; Jiang, Changjian; Huang, Xuanjing

doi:10.18653/v1/2021.acl-long.99

Cited by 6 publications

(9 citation statements)

References 40 publications

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“…Similarly, Gerrish and Blei [17] improve upon voting prediction by proposing a congress model that proxies ideological positions of legislators by linking legislative sentiment to bill texts. This model has been extended to further improve predictions of roll-call votes [7,20,25,26,32,35,44,52].…”

Section: Related Workmentioning

confidence: 99%

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

Russo¹,

Gote²,

Brandenberger³

et al. 2022

Preprint

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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 di erent 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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Section: Related Workmentioning

confidence: 99%

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

Russo¹,

Gote²,

Brandenberger³

et al. 2022

Preprint

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show abstract

“…Feng et al (2021) introduces Wikipedia corpus and constructs knowledge graphs to facilitate perspective detection. Mou et al (2021) proposes to leverage tweets, hashtags and legislative text to grasp the full picture of the political discourse. Li and Goldwasser (2021) designs pretraining tasks with social and linguistic information to augment political analysis.…”

Section: Related Workmentioning

confidence: 99%

“…We adopt the datasets and settings proposed in Mou et al (2021) to evaluate PAR and competitive baselines. Specifically, for the random setting, voting records in the 114th and 115th congresses are randomly split into 6:2:2 for training, validation, and testing.…”

Section: Datasetsmentioning

confidence: 99%

“…Modeling the perspectives of political actors has applications in various downstream tasks such as roll call vote prediction (Mou et al, 2021) and political perspective detection (Feng et al, 2021). Existing approaches generally focus on voting records or textual information of political actors to induce their stances.…”

Section: Introductionmentioning

confidence: 99%

“…2014; Gerrish and Blei, 2011) and yield promising results on the task of roll call vote prediction. For text-based methods, text analysis techniques are combined with textual information in social media posts (Li and Goldwasser, 2019), Wikipedia pages (Feng et al, 2021), legislative text (Mou et al, 2021) and news articles (Li and Goldwasser, 2021) to enrich the perspective analysis process.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

PAR: Political Actor Representation Learning with Social Context and Expert Knowledge

Feng¹,

Zhaoxuan²,

Chen³

et al. 2022

Preprint

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Modeling the ideological perspectives of political actors is an essential task in computational political science with applications in many downstream tasks. Existing approaches are generally limited to textual data and voting records, while they neglect the rich social context and valuable expert knowledge for holistic ideological analysis. In this paper, we propose PAR, a Political Actor Representation learning framework that jointly leverages social context and expert knowledge. Specifically, we retrieve and extract factual statements about legislators to leverage social context information. We then construct a heterogeneous information network to incorporate social context and use relational graph neural networks to learn legislator representations. Finally, we train PAR with three objectives to align representation learning with expert knowledge, model ideological stance consistency, and simulate the echo chamber phenomenon. Extensive experiments demonstrate that PAR is better at augmenting political text understanding and successfully advances the state-of-the-art in political perspective detection and roll call vote prediction. Further analysis proves that PAR learns representations that reflect the political reality and provide new insights into political behavior.

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Toward Efficient Legislative Processes: Analysis of Chilean Congressional Bill Votes Using Semantic Web Technologies

Cifuentes-Silva,

Astudillo,

Gayo

et al. 2024

SN COMPUT. SCI.

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Between 1990 and 2023, Chile’s Congress processed and approved 2738 laws, with an average processing time of 667.8 days from proposal to official publication. Recent political circumstances have underscored the need to identify legislative proposals that can be expedited for approval and which ones are unlikely to be approved at all. This article describes a bottom-up, data-driven classification of voting (and voters) on law proposals, which yield two axis: polarization (lack of agreement on an issue), and (political) alignment (intra-party coincidence of a group’s members regarding certain opinion). And four quadrants: “ideological stance” (high polarization, high alignment), “personal interests” (high polarization, low alignment), “thematic interest” (low polarization, low alignment), and “technical consensus” (low polarization, high alignment). We used this scheme to analyze an existing Open Linked Dataset with semantic web technologies (ontologies, RDF Shape expressions, and URI patterns), which records parliamentarians’ political parties and their voting on law proposals during 1990–2023. We found that most bills (70.14%) are in the technical consensus quadrant, and could have been quickly shepherded to approval. Wider adoption of this analysis to classify new bills may help to speed up their legislative processing, ultimately allowing Congress to serve citizens in a more timely manner.

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Align Voting Behavior with Public Statements for Legislator Representation Learning

Cited by 6 publications

References 40 publications

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

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

PAR: Political Actor Representation Learning with Social Context and Expert Knowledge

Toward Efficient Legislative Processes: Analysis of Chilean Congressional Bill Votes Using Semantic Web Technologies

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