LLM-Supported Content Analysis of Motivated Reasoning on Climate Change
For researchers studying online climate discourse, this work demonstrates LLM-assisted qualitative analysis at scale and reveals how motivated reasoning shapes engagement patterns.
This study analyzed 44,989 YouTube comments on climate change using an LLM for topic annotation and social network analysis, finding that comments on government policy and natural cycles generated significantly lower interaction than misinformation, reflecting motivated reasoning.
Public discourse around climate change remains polarized despite scientific consensus on anthropogenic climate change (ACC). This study examines how "believers" and "skeptics" of ACC differ in their YouTube comment discourse. We analyzed 44,989 comments from 30 videos using a large language model (LLM) as a qualitative annotator, identifying ten distinct topics. These annotations were combined with social network analysis to examine engagement patterns. A linear mixed-effects model showed that comments about government policy and natural cycles generated significantly lower interaction compared to misinformation, suggesting these topics are ideologically settled points within communities. These patterns reflect motivated reasoning, where users selectively engage with content that aligns with their identity and beliefs. Our findings highlight the utility of LLMs for large-scale qualitative analysis and highlight how climate discourse is shaped not only by content, but by underlying cognitive and ideological motivations.