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Modeling the ideological perspectives of political actors is an essentialtask in computational political science with applications in many downstreamtasks. Existing approaches are generally limited to textual data and votingrecords, while they neglect the rich social context and valuable expertknowledge for holistic evaluation. In this paper, we propose a representationlearning framework of political actors that jointly leverages social contextand expert knowledge. Specifically, we retrieve and extract factual statementsabout legislators to leverage social context information. We then construct aheterogeneous information network to incorporate social context and userelational graph neural networks to learn legislator representations. Finally,we train our model with three objectives to align representation learning withexpert knowledge, model ideological stance consistency, and simulate the echochamber phenomenon. Extensive experiments demonstrate that our learnedrepresentations successfully advance the state-of-the-art in three downstreamtasks. Further analysis proves the correlation between learned legislatorrepresentations and various socio-political factors, as well as bearing out thenecessity of social context and expert knowledge in modeling political actors.

Legislator Representation Learning with Social Context and Expert Knowledge