用文本分析政治立场，揭示美国参议院议题分歧与性别、地域影响。
A Structural Text-Based Scaling Model for Analyzing Political Discourse
- 基于文本构建结构化缩放模型，从发言内容推断议员意识形态
- 发现移民和枪支暴力是两党最对立的议题，堕胎讨论中女性更关注健康
- 性别和出生地比宗教信仰对立场影响更大，模型可解释性强
基于个体特征与行为对政治人物进行定位有助于刻画其群体属性及政治格局演变。本文提出结构化文本缩放（STBS）模型，从文本数据中推断发言者在潜在议题上的意识形态立场。扩展了传统的泊松因子分解主题建模框架，引入灵活的收缩先验以实现稀疏性并增强可解释性，并整合发言者特定协变量评估其与意识形态立场的关联。将STBS应用于第114届美国国会参议院演讲数据，发现移民和枪支暴力是两党间最具争议的议题；在堕胎议题讨论中，女性发言者更聚焦于女性健康；同时，发言者的出生地对其意识形态立场的影响大于宗教信仰。
原文摘要 · Abstract (English)
Scaling political actors based on their individual characteristics and behavior helps profiling and grouping them as well as understanding changes in the political landscape. In this paper we introduce the Structural Text-Based Scaling (STBS) model to infer ideological positions of speakers for latent topics from text data. We expand the usual Poisson factorization specification for topic modeling of text data and use flexible shrinkage priors to induce sparsity and enhance interpretability. We also incorporate speaker-specific covariates to assess their association with ideological positions. Applying STBS to U.S. Senate speeches from Congress session 114, we identify immigration and gun violence as the most polarizing topics between the two major parties in Congress. Additionally, we find that, in discussions about abortion, the gender of the speaker significantly influences their position, with female speakers focusing more on women's health. We also see that a speaker's region of origin influences their ideological position more than their religious affiliation.
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