Decoding Climate Disagreement: A Graph Neural Network-Based Approach to Understanding Social Media Dynamics (2024)

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.

## introduction the urgency of addressing climate change is paralleled by the complexity of discussions it evokes on social media platforms. these platforms, functioning as contemporary public squares, host diverse opinions intertwined with misinformation @xcite , posing significant challenges for distinguishing constructive debates from misleading discourse @xcite . traditional natural language processing (nlp) techniques often fall short in effectively understanding disagreements that characterize online discussions. graph neural networks (gnns), particularly graph attention networks (gats) @xcite , have emerged as potent tools for modeling relational data in complex networks. their capability to learn and represent relationships in data makes them ideally suited for modelling social media interactions, where the structure of dialogue can be as informative as the content itself. thus, we present the climatesent-gat model in this paper, which not only exploits the textual and sentimental content of communications but also captures the intricate interactions of climate discourse on social media. in the context of climate change discussions on platforms like reddit, where comment-reply pairs form a natural graph structure, our model innovatively applies gats to this domain. by focusing on the detection of disagreement in climate-related discourse, climatesent-gat aims to shed light on the patterns of communication that propagate misinformation and foster contention. the objective is twofold: to advance the methodologies of nlp by integrating them with graph-based models, and to provide actionable insights that can aid policymakers, educators, and social media platforms in fostering a more informed and rational public discourse on climate change. our study employed the climate subset from the debagreement dataset, as described in @xcite . the debagree-ment dataset was constructed by harvesting data from various subreddits using the pushshift api, which offers historical data for research purposes. to ensure the dataset only included meaningful interactions, submissions and comments with minimal engagement (fewer than a set threshold of comments or words) were excluded. this filtering aimed to focus on more impactful discussions. the resulting dataset comprised high-quality interactions that form a complex web of communication dynamics, annotated for (dis)agreement based on comment-reply contexts. the dataset is available under a creative commons attribution 4.0 international license and can be accessed via https: //scale.com/open-datasets/oxford . the subset we used was taken from the r/climate subreddit, a community dedicated to discussions on climate issues. established in 2008, r/climate has grown to encompass 99,000 members. the climate subset comprises all submissions and posts from jan 2015 to may 2021. each comment length ranging from 10 to 100 words, and for the debagreement dataset, comment-reply interactions were labelled by crowd-workers as "agree", "disagree", or "neutral". we used this dataset for the same three-way classification task, to evaluate our model's capability to detect disagreements within climate changerelated comment-reply pairs. we demonstrate superior performance compared to pre-existing models (see ## related work several notable works precede ours in using graphbased approaches on the debagreement dataset. @xcite use a community-detection algorithm on social networks defined by the interactions, in order to compare the dynamics of polarization in different subreddit communities. lorge et al. (2024) successfully predicted disagreements in a slice of the deba-greement dataset, using a signed graph convolutional network (sgcn) applied to a a bipartite graph organized around the stance of users towards selected named entities. this study showcases the ability of gnns to discern subtleties in discourse relations that traditional models often miss. our climatesent-gat achieves better generality by leveraging not only the structural data but also more extensive textual information. we employ graph attention networks (gats) instead of other graph neural networks, because of their dynamic edge weighting and fine-grained attention capabilities. these features enable the model to adaptively focus on the most relevant interactions within social media discussions, which is crucial for accurately detecting disagreements and integrating diverse data types such as textual embeddings and sentiment scores.