@inproceedings{su-pierrehumbert-2024-decoding,
title = "Decoding Climate Disagreement: A Graph Neural Network-Based Approach to Understanding Social Media Dynamics",
author = "Su, Ruiran and
Pierrehumbert, Janet",
editor = "Stammbach, Dominik and
Ni, Jingwei and
Schimanski, Tobias and
Dutia, Kalyan and
Singh, Alok and
Bingler, Julia and
Christiaen, Christophe and
Kushwaha, Neetu and
Muccione, Veruska and
A. Vaghefi, Saeid and
Leippold, Markus",
booktitle = "Proceedings of the 1st Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.climatenlp-1.5",
doi = "10.18653/v1/2024.climatenlp-1.5",
pages = "63--81",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Decoding Climate Disagreement: A Graph Neural Network-Based Approach to Understanding Social Media Dynamics
%A Su, Ruiran
%A Pierrehumbert, Janet
%Y Stammbach, Dominik
%Y Ni, Jingwei
%Y Schimanski, Tobias
%Y Dutia, Kalyan
%Y Singh, Alok
%Y Bingler, Julia
%Y Christiaen, Christophe
%Y Kushwaha, Neetu
%Y Muccione, Veruska
%Y A. Vaghefi, Saeid
%Y Leippold, Markus
%S Proceedings of the 1st Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2024)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F su-pierrehumbert-2024-decoding
%X 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.
%R 10.18653/v1/2024.climatenlp-1.5
%U https://aclanthology.org/2024.climatenlp-1.5
%U https://doi.org/10.18653/v1/2024.climatenlp-1.5
%P 63-81
Markdown (Informal)
[Decoding Climate Disagreement: A Graph Neural Network-Based Approach to Understanding Social Media Dynamics](https://aclanthology.org/2024.climatenlp-1.5) (Su & Pierrehumbert, ClimateNLP-WS 2024)
ACL