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

TL;DR

This paper presents the ClimateSent-GAT model that combines Graph Attention Networks with NLP techniques to classify and predict climate-related disagreements on Reddit, improving understanding of social media dynamics.

Contribution

It introduces a novel GAT-based approach for classifying social media disagreements, integrating graph structures with NLP for better accuracy.

Findings

Outperforms existing benchmarks in disagreement classification

Effectively captures complex interaction and sentiment dynamics

Provides insights for climate science communication

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.