# Artificial Intelligence and Civil Discourse: How LLMs Moderate Climate Change Conversations

arXiv: https://arxiv.org/abs/2506.12077 · HTML: https://mlsanity.com/paper/2506.12077/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 66 references that are in the map.
- First posted on arXiv 7 Jun 2025.

## Builds on (most cited first, 12 of 12)

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- [Exploring Large Language Models for Communication Games: An Empirical Study on Werewolf](https://mlsanity.com/paper/2309.04658/) (2023)
- [War and Peace (WarAgent): Large Language Model-based Multi-Agent Simulation of World Wars](https://mlsanity.com/paper/2311.17227/) (2023)
- [When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunities](https://mlsanity.com/paper/2307.16376/) (2023)
- [BASES: Large-scale Web Search User Simulation with Large Language Model based Agents](https://mlsanity.com/paper/2402.17505/) (2024)
- [Affective Polarization and Dynamics of Information Spread in Online Networks](https://mlsanity.com/paper/2310.18553/) (2023)
- [ConflictLens: LLM-Based Conflict Resolution Training in Romantic Relationship](https://mlsanity.com/paper/2505.11715/) (2025)
- [Towards Understanding Cyberbullying Behavior in a Semi-Anonymous Social Network](https://mlsanity.com/paper/1404.3839/) (2014)

## Abstract (arXiv)

As large language models (LLMs) become increasingly integrated into online platforms and digital communication spaces, their potential to influence public discourse - particularly in contentious areas like climate change - requires systematic investigation. This study examines how LLMs naturally moderate climate change conversations through their distinct communicative behaviors. We conduct a comparative analysis of conversations between LLMs and human users on social media platforms, using five advanced models: three open-source LLMs (Gemma, Llama 3, and Llama 3.3) and two commercial systems (GPT-4o by OpenAI and Claude 3.5 by Anthropic). Through sentiment analysis, we assess the emotional characteristics of responses from both LLMs and humans. The results reveal two key mechanisms through which LLMs moderate discourse: first, LLMs consistently display emotional neutrality, showing far less polarized sentiment than human users. Second, LLMs maintain lower emotional intensity across contexts, creating a stabilizing effect in conversations. These findings suggest that LLMs possess inherent moderating capacities that could improve the quality of public discourse on controversial topics. This research enhances our understanding of how AI might support more civil and constructive climate change discussions and informs the design of AI-assisted communication tools.