Decoding Climate Disagreement: A Graph Neural Network-Based Approach to Understanding Social Media Dynamics
Keywords: GNN, GAT, Disagreement Detection, Social Media Analysis, Machine Learning in Climate Science
TL;DR: This paper introduces the ClimateSent-GAT Model, an innovative application of Graph Attention Networks (GATs) integrated with advanced NLP techniques to effectively detect and classify types of disagreements within Reddit comment-reply pairs.
Abstract: This paper presents the ClimateSent-GAT Model, a novel approach that combines Graph Attention Networks (GATs) with natural language processing techniques 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.
Archival Submission: arxival
Arxival Submission: arxival
Submission Number: 8
Loading