  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.
