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.- Anthology ID:
- 2024.climatenlp-1.5
- Volume:
- Proceedings of the 1st Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2024)
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Dominik Stammbach, Jingwei Ni, Tobias Schimanski, Kalyan Dutia, Alok Singh, Julia Bingler, Christophe Christiaen, Neetu Kushwaha, Veruska Muccione, Saeid A. Vaghefi, Markus Leippold
- Venues:
- ClimateNLP | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 63–81
- Language:
- URL:
- https://aclanthology.org/2024.climatenlp-1.5
- DOI:
- Cite (ACL):
- Ruiran Su and Janet Pierrehumbert. 2024. Decoding Climate Disagreement: A Graph Neural Network-Based Approach to Understanding Social Media Dynamics. In Proceedings of the 1st Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2024), pages 63–81, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- Decoding Climate Disagreement: A Graph Neural Network-Based Approach to Understanding Social Media Dynamics (Su & Pierrehumbert, ClimateNLP-WS 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.climatenlp-1.5.pdf