Modeling political actors is at the core of quantitative political science. Existing works have incorporated contextual information to better learn the representation of political actors for specific tasks through graph models. However, they are limited to the structure and objective of training settings and can not be generalized to all politicians and other tasks. In this paper, we propose a Unified Pre-training Architecture for Political Actor Modeling based on language (UPPAM). In UPPAM, we aggregate statements to represent political actors and learn the mapping from languages to representation, instead of learning the representation of particular persons. We further design structure-aware contrastive learning and behavior-driven contrastive learning tasks, to inject multidimensional information in the political context into the mapping. In this framework, we can profile political actors from different aspects and solve various downstream tasks. Experimental results demonstrate the effectiveness and capability of generalization of our method.

Framing is a communication strategy to bias discussion by selecting and emphasizing. Frame detection aims to automatically analyze framing strategy. Previous works on frame detection mainly focus on a single scenario or issue, ignoring the special characteristics of frame detection that new events emerge continuously and policy agenda changes dynamically. To better deal with various context and frame typologies across different issues, we propose a two-stage adaptation framework. In the framing domain adaptation from pre-training stage, we design two tasks based on pivots and prompts to learn a transferable encoder, verbalizer, and prompts. In the downstream scenario generalization stage, the transferable components are applied to new issues and label sets. Experiment results demonstrate the effectiveness of our framework in different scenarios. Also, it shows superiority both in full-resource and low-resource conditions.

Ideology of legislators is typically estimated by ideal point models from historical records of votes. It represents legislators and legislation as points in a latent space and shows promising results for modeling voting behavior. However, it fails to capture more specific attitudes of legislators toward emerging issues and is unable to model newly-elected legislators without voting histories. In order to mitigate these two problems, we explore to incorporate both voting behavior and public statements on Twitter to jointly model legislators. In addition, we propose a novel task, namely hashtag usage prediction to model the ideology of legislators on Twitter. In practice, we construct a heterogeneous graph for the legislative context and use relational graph neural networks to learn the representation of legislators with the guidance of historical records of their voting and hashtag usage. Experiment results indicate that our model yields significant improvements for the task of roll call vote prediction. Further analysis further demonstrates that legislator representation we learned captures nuances in statements.