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.

用户建模已经引起了学术界和工业界的广泛关注。现有的方法大多侧重于将用户间的人际关系融入社区,而用户生成的内容(如帖子)却没有得到很好的研究。在本文中,我们通过实际舆情传播相关的分析表明,在舆情传播过程中对用户属性进行研究的重要作用,并且提出了用户资料数据的筛选方法。同时,我们提出了一种通过异构多质心图池为用户捕获更多不同社区特征的建模。我们首先构造了一个由用户和关键字组成的异质图,并在其上采用了一个异质图神经网络。为了方便用户建模的图表示,提出了一种多质心图池化机制,将多质心的集群特征融入到表示学习中。在三个基准数据集上的大量实验表明了该方法的有效性。