Polarization has increased substantially in political discourse, contributing
to a widening partisan divide. In this paper, we analyzed large-scale,
real-world language use in Reddit communities (294,476,146 comments) and in
news outlets (6,749,781 articles) to uncover psychological dimensions along
which partisan language is divided. Using word embedding models that captured
semantic associations based on co-occurrences of words in vast textual corpora,
we identified patterns of affective polarization present in natural political
discourse. We then probed the semantic associations of words related to seven
political topics (e.g., abortion, immigration) along the dimensions of morality
(moral-to-immoral), threat (threatening-to-safe), and valence
(pleasant-to-unpleasant). Across both Reddit communities and news outlets, we
identified a small but systematic divergence in the moral associations of words
between text sources with different partisan leanings. Moral associations of
words were highly correlated between conservative and liberal text sources
(average $\rho$ = 0.96), but the differences remained reliable to enable us to
distinguish text sources along partisan lines with above 85% classification
accuracy. These findings underscore that despite a shared moral understanding
across the political spectrum, there are consistent differences that shape
partisan language and potentially exacerbate political polarization. Our
results, drawn from both informal interactions on social media and curated
narratives in news outlets, indicate that these trends are widespread.
Leveraging advanced computational techniques, this research offers a fresh
perspective that complements traditional methods in political attitudes.

研究通过分析 Reddit 社群和新闻媒体等大规模真实语言使用数据，使用词嵌入模型发现了政治性语言中存在的情感分化模式以及与七个政治话题相关的词语的语义关联，揭示了跨党派界限的道德联想差异，这些结果强调了虽然在政治谱系上有着共享的道德理解，但仍然存在着塑造党派语言并潜在加剧政治极化的一致差异。