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

TL;DR

This study shows that ChatGPT amplifies ideological polarization in online political discussions by echoing viewpoints, yet surprisingly reduces hostility and toxicity, indicating a complex impact on online discourse.

Contribution

It reveals how ChatGPT-generated comments reinforce ideological divides while decreasing affective polarization, challenging assumptions about extremity and incivility in online discussions.

Findings

ChatGPT increases ideological polarization by echoing viewpoints.

Affective polarization, hostility, and toxicity decrease despite ideological divides.

ChatGPT's pattern of echoing viewpoints is consistent with algorithmic sycophancy.

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.