Large Language Models Polarize Ideologically but Moderate Affectively in Online Political Discourse
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866915758689222656 |
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| author | Wang, Gavin Anbudurai, Srinaath Sun, Oliver Li, Xitong Wu, Lynn |
| author_facet | Wang, Gavin Anbudurai, Srinaath Sun, Oliver Li, Xitong Wu, Lynn |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_20238 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Large Language Models Polarize Ideologically but Moderate Affectively in Online Political Discourse Wang, Gavin Anbudurai, Srinaath Sun, Oliver Li, Xitong Wu, Lynn General Economics Economics 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. |
| title | Large Language Models Polarize Ideologically but Moderate Affectively in Online Political Discourse |
| topic | General Economics Economics |
| url | https://arxiv.org/abs/2601.20238 |