# Moral consensus and divergence in partisan language use

arXiv: https://arxiv.org/abs/2310.09618 · HTML: https://mlsanity.com/paper/2310.09618/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 92 references that are in the map.
- First posted on arXiv 14 Oct 2023.

## Builds on (most cited first, 16 of 16)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [Semantics derived automatically from language corpora contain human-like biases](https://mlsanity.com/paper/1608.07187/) (2016)
- [The Pushshift Reddit Dataset](https://mlsanity.com/paper/2001.08435/) (2020)
- [Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes](https://mlsanity.com/paper/1711.08412/) (2017)
- [The Geometry of Culture: Analyzing Meaning through Word Embeddings](https://mlsanity.com/paper/1803.09288/) (2018)
- [Quantifying social organization and political polarization in online platforms](https://mlsanity.com/paper/2010.00590/) (2020)
- [SemAxis: A Lightweight Framework to Characterize Domain-Specific Word Semantics Beyond Sentiment](https://mlsanity.com/paper/1806.05521/) (2018)
- [FrameAxis: Characterizing Microframe Bias and Intensity with Word Embedding](https://mlsanity.com/paper/2002.08608/) (2020)
- [Examining the consumption of radical content on YouTube](https://mlsanity.com/paper/2011.12843/) (2020)
- [Machine learning as a model for cultural learning: Teaching an algorithm what it means to be fat](https://mlsanity.com/paper/2003.12133/) (2020)
- [Interpretable Word Embeddings via Informative Priors](https://mlsanity.com/paper/1909.01459/) (2019)
- [When the Echo Chamber Shatters: Examining the Use of Community-Specific Language Post-Subreddit Ban](https://mlsanity.com/paper/2106.16207/) (2021)
- [Political Discussion is Abundant in Non-political Subreddits (and Less Toxic)](https://mlsanity.com/paper/2104.09560/) (2021)
- [United States Politicians' Tone Became More Negative with 2016 Primary Campaigns](https://mlsanity.com/paper/2207.08112/) (2022)

## Abstract (arXiv)

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 ρ = 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.