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

TL;DR

PAR is a novel framework that integrates social context and expert knowledge through graph neural networks to improve ideological analysis and prediction of political actors, surpassing existing text-based methods.

Contribution

It introduces a joint learning approach combining social context, expert knowledge, and ideological modeling using relational graph neural networks.

Findings

Outperforms state-of-the-art in political perspective detection.

Enhances roll call vote prediction accuracy.

Learns representations reflecting political reality.

Abstract

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 \textbf{PAR}, a \textbf{P}olitical \textbf{A}ctor \textbf{R}epresentation 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.