PAR : Political Actor Representation Learning with Social Context and Expert Knowledge (2022)

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 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.

## introduction modeling the perspectives of political actors has applications in various downstream tasks such as roll call vote prediction @xcite and political perspective detection @xcite . existing approaches generally focus on voting records or textual information of political actors to induce their stances. ideal point model @xcite is one of the most widely used approach for vote-based analysis, while later works enhance the ideal point model @xcite @xcite 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 @xcite , wikipedia pages @xcite , legislative text @xcite and news articles @xcite to enrich the perspective analysis process. however, existing methods fail to incorporate the rich social context and valuable expert knowledge of political actors. as illustrated in figure 1 , social context information such as home state and party affiliation serves as background knowledge and helps connect different political actors @xcite . these social context facts about political actors also differentiate them and indicate their ideological stances. in addition, expert knowledge from political think tanks provides valuable insights and helps to anchor the perspective analysis process. as a result, political actor representation learning should be guided by domain expertise to facilitate downstream tasks in computational political science. that being said, social context and expert knowledge should be incorporated in modeling legislators to ensure a holistic evaluation. in light of these challenges, we propose par, a legislator representation learning framework that jointly leverages social context and expert knowledge. we firstly collect a dataset of political actors by retrieving and extracting social context information from their wikipedia homepages and adapting expert knowledge from two political think tanks afl-cio foot_0 and heritage action foot_1 . after that, we construct a heterogeneous information network to model social context information and adopt relational graph neural networks for representation learning. finally, we train the framework with three training objectives to leverage expert knowledge, model social and political phenomena, and learn representations of political actors in the process. we evaluate par on two computational political science tasks and examine the political behaviour of learned representations. our main contributions are summarized as follows: ## related work the ideological perspectives of political actors play an essential role in their individual behavior and adds up to influence the overall legislative process @xcite @xcite . political scientists first explored to quantitatively model political actors based on their voting behaviour. ideal point model @xcite is one of the earliest approach to leverage voting records to analyze their perspectives. it projects political actors and legislation onto onedimensional spaces and measure distances. many works later extended the ideal point model. gerrish and blei (2011) leverages bill content to infer legislator perspectives. gu et al. (2014) introduces topic factorization to model voting behaviour on different issues to establish a fine-grained approach. kraft et al. (2016) models legislators with multidimensional ideal vectors to analyze voting records. mohammad et al. (2017) and küçük and can (2018) use svm and handcrafted text features to augment the analysis. in addition to voting, textual data such as speeches and public statements are also leveraged to model political perspectives @xcite @xcite @xcite @xcite @xcite @xcite @xcite . @xcite leverage message streams to inference users political preference. @xcite propose to better understand political stances by analyzing politicians' tweet and their temporal activities. @xcite propose to analyze twitter posts to better understand news stances. @xcite and kawintiranon and singh (2021) leverage pre-trained language model for stance prediction. @xcite uses conditional lstm to encode tweets for stance detection. furthermore, cnn @xcite and hierarchical attention networks @xcite ) are adopted for stance detection. @xcite proposes an unsupervised framework for user representation learning and stance detection. @xcite proposes to jointly model legislators and legislations for vote prediction. @xcite introduces wikipedia corpus and constructs knowledge graphs to facilitate perspective detection. @xcite proposes to leverage tweets, hashtags and legislative text to grasp the full picture of the political discourse. @xcite designs pretraining tasks with social and linguistic information to augment political analysis. however, these vote and text-based methods fail to leverage the rich social context of political actors and the valuable expert knowledge of political think tanks. in this paper, we aim to incorporate social context and expert knowledge while focusing on learning representations of political actors to model their perspectives and facilitate downstream tasks. ## methodology figure 2 presents an overview of par (political actor representation learning). we firstly collect a dataset of political actors from wikipedia and political think tanks. we then construct a heterogeneous political think tanks political think tanks expert knowledge gated r-gcn res. gate gated r-gcn res. gate legislator scoreboards afl-cio score heritage action score model training political actors political actors heterogeneous information network wikipedia wikipedia social context party affiliation elected office home state party affiliation elected office home state party affiliation elected office home state party affiliation elected office home state political actor representation learning training objective 1: expert knowledge mlp u.s. sen. elizabeth warren d-ma training objective 1: expert knowledge mlp u.s. sen. elizabeth warren d-ma training objective 2: stance consistency u.s. sen. elizabeth warren d-ma training objective 2: stance consistency u.s. sen. elizabeth warren d-ma training objective 3: echo chamber training objective 3: echo chamber information network to jointly model political actors and social context information. finally, we learn graph representations with gated relational graph convolutional networks (gated r-gcn) and train the framework with three different objectives to leverage expert knowledge and model various socio-political phenomena. ## experiment after learning political actor representations with par, we study whether they are effective in computational political science and reveal real-world political behavior. specifically, we test out par on political perspective detection and roll call vote prediction, two political text understanding tasks that emphasizes political actor modeling. we leverage par in these tasks to examine whether it could contribute to the better understanding of political text. we then study the learned representations of par and whether they provide new insights into real-world politics. ## conclusion in this paper, we present par, a framework to learn representations of political actors with social context and expert knowledge. specifically, we retrieve social context information from wikipedia and expert knowledge from political think tanks, construct a hin to model legislators and learn representations with gated r-gcns and three training objectives. extensive experiments demonstrate that par advances the state-of-the-art in political perspective detection and roll call vote prediction. par further provides novel insights into political actors through its representation learning process.