Large Language Models Polarize Ideologically but Moderate Affectively in Online Political Discourse

Gavin Wang, Srinaath Anbudurai, Oliver Sun, Xitong Li and Lynn Wu

Papers from arXiv.org

Abstract: The emergence of large language models (LLMs) is reshaping how people engage in political discourse online. We examine how the release of ChatGPT altered ideological and emotional patterns in the largest political forum on Reddit. Analysis of millions of comments shows that ChatGPT intensified ideological polarization: liberals became more liberal, and conservatives more conservative. This shift does not stem from the creation of more persuasive or ideologically extreme original content using ChatGPT. Instead, it originates from the tendency of ChatGPT-generated comments to echo and reinforce the viewpoint of original posts, a pattern consistent with algorithmic sycophancy. Yet, despite growing ideological divides, affective polarization, measured by hostility and toxicity, declined. These findings reveal that LLMs can simultaneously deepen ideological separation and foster more civil exchanges, challenging the long-standing assumption that extremity and incivility necessarily move together.

Date: 2026-01
New Economics Papers: this item is included in nep-ain and nep-pol
References: Add references at CitEc
Citations:

Downloads: (external link)
http://arxiv.org/pdf/2601.20238 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2601.20238

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().