Deep Ordinal Regression for Pledge Specificity Prediction

TL;DR

This paper introduces a new dataset of political pledges annotated for specificity, and proposes deep ordinal regression models to predict pledge detail levels, aiding political analysis and ideology prediction.

Contribution

It creates a novel, large-scale pledge dataset and develops deep ordinal regression methods for pledge specificity prediction, advancing computational political analysis.

Findings

Deep ordinal regression outperforms baseline models.

Pledge specificity improves ideology prediction accuracy.

Qualitative analysis reveals party-specific issue salience.

Abstract

Many pledges are made in the course of an election campaign, forming important corpora for political analysis of campaign strategy and governmental accountability. At present, there are no publicly available annotated datasets of pledges, and most political analyses rely on manual analysis. In this paper we collate a novel dataset of manifestos from eleven Australian federal election cycles, with over 12,000 sentences annotated with specificity (e.g., rhetorical vs.\ detailed pledge) on a fine-grained scale. We propose deep ordinal regression approaches for specificity prediction, under both supervised and semi-supervised settings, and provide empirical results demonstrating the effectiveness of the proposed techniques over several baseline approaches. We analyze the utility of pledge specificity modeling across a spectrum of policy issues in performing ideology prediction, and further provide qualitative analysis in terms of capturing party-specific issue salience across election cycles.