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

Xiaoning (Gavin) Wang, Srinaath Anbu Durai, Oliver Sun, Xitong Li and Lynn Wu
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Xiaoning (Gavin) Wang: Purdue University - Daniels School of Business
Srinaath Anbu Durai: HEC Paris
Oliver Sun: University of Pennsylvania - The Wharton School
Xitong Li: HEC Paris
Lynn Wu: University of Pennsylvania

No 1653, HEC Research Papers Series from HEC Paris

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 platformwide 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.

Keywords: Large Language Models; ChatGPT; Political Polarization; Liberal; Conservative; Affective Polarization (search for similar items in EconPapers)
JEL-codes: D72 D91 L86 O33 (search for similar items in EconPapers)
Pages: 62 pages
Date: 2026-08-17
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Persistent link: https://EconPapers.repec.org/RePEc:ebg:heccah:1653

DOI: 10.2139/ssrn.7279418

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