Decoding Climate Disagreement: A Graph Neural Network-Based Approach to Understanding Social Media Dynamics

TL;DR

ClimateSent-GAT targets predicting disagreement in climate-related discourse on Reddit by integrating textual embeddings, entity-aware sentiment, and a graph attention network over comment-reply threads. The approach uses the Climate subset of the DEBAGREEMENT dataset and a climate-entity list to produce a three-way classification (Disagree, Neutral, Agree) with superior performance over BERT/RoBERTa baselines. The authors provide interpretability through attention-weight analysis and feature ablation, and demonstrate entity-level patterns such as how certain entities correlate with disagreement and sentiment. The work advances graph-based NLP for climate communication and offers actionable insights for policymakers, educators, and platform designers.

Abstract

This work introduces the ClimateSent-GAT Model, an innovative method that integrates Graph Attention Networks (GATs) with techniques from natural language processing to accurately identify and predict disagreements within Reddit comment-reply pairs. Our model classifies disagreements into three categories: agree, disagree, and neutral. Leveraging the inherent graph structure of Reddit comment-reply pairs, the model significantly outperforms existing benchmarks by capturing complex interaction patterns and sentiment dynamics. This research advances graph-based NLP methodologies and provides actionable insights for policymakers and educators in climate science communication.

Figures (5)

• Figure 1: Diagram for the pipeline of the ClimateSent-GAT model
• Figure 2: Attention weights to the climate interactions
• Figure 3: Parent and Child Sentiment by Climate-Related Entities
• Figure 4: Parent sentiment for entities with least vs most disagreement
• Figure 5: Average Sentiment Attention Scores for Different Entity Categories