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

These authors contributed equally to this work. Abstract --As Large Language Models (LLMs) become increasingly integrated into online platforms and digital communication spaces, their potential to influence public discourse--particularly in contentious domains like climate change--demands systematic investigation. This study examines how LLMs naturally moderate climate change conversations through their distinct communicative behaviors, offering insights into their role as facilitators of civil discourse. We conducted a comparative analysis of conversational patterns between LLMs and human participants in climate change discussions across social media platforms. Our investigation employed five state-of-the-art 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 assessed the emotional characteristics and discourse patterns exhibited by both LLMs and human users. Our findings reveal two key mechanisms through which LLMs moderate climate change conversations: First, LLMs consistently demonstrate emotional neutrality, with their responses significantly dominated by neutral sentiment compared to human participants who exhibit more polarized emotional expressions. Second, LLMs maintain notably lower emotional intensity across all interaction contexts, creating a stabilizing effect on conversational dynamics. These results suggest that LLMs possess inherent moderating capabilities that could enhance the quality of public discourse on controversial topics. By maintaining emotional equilibrium and reducing inflammatory rhetoric, LLMs may serve as valuable tools for fostering more constructive and civil climate change conversations online. This research contributes to our understanding of AI's potential role in improving digital discourse and offers implications for the design of AI-mediated communication platforms.