  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.
