Political Ideology and Polarization of Policy Positions: A Multi-dimensional Approach

TL;DR

This paper introduces a multi-dimensional, diachronic dataset of news articles annotated for ideological positions, enabling nuanced analysis of political polarization over time and across issues.

Contribution

It presents the first dataset of news articles with paragraph-level ideological annotations and models for predicting ideology, advancing the understanding of political polarization.

Findings

Controlled for author stance, enabling measurement of ideological polarization.

Demonstrated the feasibility of quantitative, temporal analysis of multidimensional ideology.

Outlined baseline models for ideology prediction as a distinct task.

Abstract

Analyzing ideology and polarization is of critical importance in advancing our grasp of modern politics. Recent research has made great strides towards understanding the ideological bias (i.e., stance) of news media along the left-right spectrum. In this work, we instead take a novel and more nuanced approach for the study of ideology based on its left or right positions on the issue being discussed. Aligned with the theoretical accounts in political science, we treat ideology as a multi-dimensional construct, and introduce the first diachronic dataset of news articles whose ideological positions are annotated by trained political scientists and linguists at the paragraph level. We showcase that, by controlling for the author's stance, our method allows for the quantitative and temporal measurement and analysis of polarization as a multidimensional ideological distance. We further present baseline models for ideology prediction, outlining a challenging task distinct from stance detection.