Moral consensus and divergence in partisan language use
Nakwon Rim Marc G. Berman Yuan Chang Leong
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.