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 Reddit's largest political forum. Analysis of millions of comments shows that ChatGPT intensified ideological polarization: liberal-leaning authors posted increasingly liberal comments, while conservative-leaning authors posted increasingly conservative comments. Multiple falsification tests suggest that these findings are unlikely to be driven by contemporaneous events, such as the 2022 U.S. midterm elections, or by broader platform-wide trends in political polarization. Mechanism tests show that this shift does not stem from the creation of more persuasive or ideologically extreme original content using LLM. Instead, it originates from the tendency of LLM-assisted comments to echo and reinforce the original post's viewpoint, 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 in literature that extremity and incivility necessarily move together.

Date: 2026-01, Revised 2026-08
New Economics Papers: this item is included in nep-ain, nep-big and nep-pol
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