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

Ruiran Su
Department of Engineering Science
University of Oxford
<EMAIL_ADDRESS>
&Janet B. Pierrehumbert
Oxford e-Research Centre
University of Oxford
<EMAIL_ADDRESS>

###### 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.

##  1 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 Diggelmann et al. (2020a), posing significant challenges for
distinguishing constructive debates from misleading discourse Johansson et al.
(2023). 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)
Veličković et al. (2018), 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.

Statistics | r/climate
---|---
Start Date | January 2015
End Date | May 2021
Number of Posts | 2367
Number of Users | 4,580
Comment-Reply Interactions | 5,773
Interactions labelled as Agree | 32%
Interactions labelled as Neutral | 28%
Interactions labelled as Disagree | 40%
Table 1: Dataset statistics for the r/climate subreddit.

Our study employed the Climate subset from the DEBAGREEMENT dataset, as
described in Pougué-Biyong et al. (2021). The DEBAGREEMENT 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 change-
related comment-reply pairs. We demonstrate superior performance compared to
pre-existing models (see Table 2).

##  2 Literature Review

###  2.1 Graph Neural Networks in Understanding Social Dynamics

Graph Neural Networks (GNNs) have emerged as a powerful tool for understanding
complex network structures. One of the foundational works in applying GNNs to
social networks is Kipf and Welling (2017), who demonstrated how Graph
Convolutional Networks (GCNs) could be used to classify nodes in citation
networks. Building on this approach, researchers have adapted similar models
to more complex social structures, such as user interactions on social media
platforms Hamilton et al. (2017). These studies show that GNNs can effectively
model relationships and interactions, leading to improved performance in tasks
like community detection and influence prediction.

Graph Attention Networks (GATs), introduced by Veličković et al. (2018),
further enhance this capability by incorporating attention mechanisms that
weigh the influence of neighboring nodes. This feature is particularly useful
in social media contexts, where the relevance and influence of a comment can
vary significantly based on the interaction dynamics. For instance, Abu-El-
Haija et al. (2019) leveraged GATs to predict the future state of users in
dynamic social networks, effectively mapping how interactions influence user
behavior over time. Moreover, GATs can be deployed to tackle more direct
social issues. For example, Gao et al. Gao et al. (2022) used GATs to study
the diffusion of information in online social networks, identifying key
patterns that signify misinformation spread. This is directly relevant to
fields like climate science, where misinformation can have significant real-
world impacts.

Overall, the integration of GNNs into the analysis of social media dynamics
offers a promising perspective for not only detecting and understanding social
interactions but also for intervening in a timely manner to guide discussions
towards more constructive outcomes. The ongoing development in this field
suggests a growing potential for GNNs to contribute significantly to our
understanding of digital communication landscapes, especially in contentious
domains like climate science where the clarity and accuracy of information are
paramount.

###  2.2 Graph Neural Networks in NLP

Graph Neural Networks (GNNs) continue to make significant strides in the field
of Natural Language Processing (NLP), providing advanced solutions to complex
problems where traditional methods fall short. The adaptability of GNNs to
encode relationships within data makes them particularly effective for tasks
involving rich contextual and relational information.

Advancements in semantic role labelling leverage GNNs to incorporate deep
contextual embeddings. Marcheggiani and Titov (2017) present a novel approach
that integrates contextual information with GNNs, enhancing the model’s
ability for semantic labelling.

Wang et al. (2021) introduce a cross-lingual graph neural network that models
syntactic and semantic relationships across languages. This framework
significantly improves text classification accuracy, particularly for low-
resource languages, by capturing and utilizing the inherent linguistic
structures across different language families.

Ghosal et al. (2020) developed DialogueGCN, a graph convolutional network
tailored for emotion recognition in conversations. This model recognizes and
interprets the emotional dynamics in dialogues by structuring the dialogue as
a graph where nodes represent utterances and edges define the interaction
dynamics, leading to more nuanced and accurate emotion recognition.

Expanding the use of GNNs to document-level tasks, Yasunaga et al. (2017)
explored multi-document summarization through a graph-based approach. Their
model, which constructs graphs representing relationships between sentences
across documents, demonstrates improved performance in identifying key
information and generating coherent summaries, showcasing the potential of
GNNs to manage and synthesize information from multiple text sources.

These applications highlight the versatility and robustness of GNNs in
tackling diverse NLP challenges. By effectively capturing and processing
relational data, GNNs not only improve the performance of NLP systems but also
open new avenues for research and development in the field.

Given the hierarchical and interconnected nature of social media threads, GNNs
offer a promising avenue for disagreement detection, which we exploit in our
ClimateSent-GAT model.

###  2.3 Disagreement Detection and Modeling Social Interactions

Disagreement detection in online discussions is a critical area of research in
NLP that has seen substantial advancements with the incorporation of machine
learning techniques, particularly GNNs. This section explores the latest
methodologies for modeling social interactions and detecting disagreements,
emphasizing the integration of sophisticated NLP tools with social network
analysis.

Early works in disagreement detection leveraged techniques like sentiment
analysis and opinion mining Pang and Lee (2008). With the maturation of the
field, more nuanced techniques such as argumentation mining emerged Cabrio and
Villata (2017).

Recent studies have pushed the boundaries of disagreement detection by
employing advanced machine learning frameworks that integrate GNNs with other
deep learning techniques. Huang et al. Huang et al. (2021) utilized a
Recurrent Graph Neural Networks (RGNN) to effectively identify disagreement in
online forums. The RGNN model captures both the textual content and the
relational dynamics between participants, leading to a more nuanced
understanding of disagreement.

Climate science discussions, given their polarized nature, make the
understanding of disagreement indispensable. The DEBAGREEMENT dataset and the
Stance Embeddings Model by Pougué-Biyong et al. Pougué-Biyong et al. (2023)
have laid the groundwork in this specific domain.

These developments represent a leap forward in our ability to not only detect
but also interpret and respond to disagreements in digital communication.
However, most existing approaches have focused solely on textual features,
missing out on the rich contextual cues available in the conversational
structure of social media threads.

##  3 Experiments

###  3.1 Climate-related Entities Compilation

This research uses the Climate subset of DEBAGREEMENT. Our goal is to train a
hybrid model which exploits both user interactions and sentiment features in
the discourse towards climate-related entities. Thus, we firstly executed
Named Entity Recognition (NER) using the SpaCy model. Then, we filtered out
entities from non-relevant categories such as cardinal numbers, dates, and
monetary values. The remained entities are still messy, so we manually
compiled a climate-related entity list (see Appendix A) based on automatically
extracted entities. We ended up having 1397 climate-related entities for
further experiments.

###  3.2 ClimateSent-GAT Model Construction

In this paper, we introduce a hybrid model architecture that leverages textual
embeddings, sentiment scores and Graph Attention Networks (GATs) to capture
contextual and semantic information effectively. In the realm of climate
science discussions, understanding the social relations and sentiment
interactions towards climate entities in comment-reply structures can offer
profound insights into public perceptions and discourse dynamics. Our
ClimateSent-GAT model, a specialized variant of the Graph Attention Network,
is tailored to capture these intricate sentiment relationships.

The choice of GATs over other types of GNNs such as Graph Convolutional
Networks (GCNs) or Graph Recurrent Networks (GRNs) was motivated by several
key considerations:

  * •

Dynamic Edge Weighting: Unlike GCNs, which utilize fixed weights for edges
based on the graph structure, GATs dynamically compute the weights through
attention mechanisms. This adaptability is essential for social media, where
the relevance of comments can significantly vary based on the context and
interaction dynamics.

  * •

Fine-Grained Attention: GATs provide fine-grained control over information
flow between nodes (e.g., comments and replies), focusing on the most
informative parts of the data. This feature is crucial for environments like
online forums, where not all interactions directly contribute to the outcomes
of sentiment or disagreement detection.

  * •

Robustness to Sparse Data: Online discussions are often characterized by
sparsity and uneven distribution. GATs excel in these settings by
concentrating attention on significant nodes and edges, thus enhancing the
model’s predictive accuracy and reducing background noise.

  * •

Enhanced Feature Integration: The architecture of GATs allows for a nuanced
integration of node and edge features, such as textual embeddings and
sentiment scores. This integration is vital for detecting subtle cues that
signify agreement or disagreement in communication.

In this section, we’ll further introduce the details of the model.

####  3.2.1 Feature Engineering

We utilize a multi-faceted feature engineering approach that combines
transformer-based sentence embeddings and sentiment scores to form a robust
and contextually rich representation of social media dialogues.

To encode the textual features of the dataset, we utilize Sentence-BERT
(paraphrase-MiniLM-L6-v2). The model generates 384-dimensional vectors that
capture semantic meanings and syntactic structures for each sentence in the
dialogue. The textual embeddings are then utilized as node features in our
graph-structured data.

Sentiment analysis has been effectively applied to understand public opinion
and user-generated content Cabrio and Villata (2017). To further enrich our
feature set, we incorporate entity-based sentiment scores towards the climate-
related entities for both parent and child messages in a conversation thread.
For each climate-related entity identified in the text, a snippet comprising
30 characters before and after the entity mention was extracted. The sentiment
scores serve as an additional source of information, capturing the emotional
tone and nuance in the dialogues, which is pivotal in discerning disagreement
or agreement among users.

We firstly extracted the comment-reply pairs which mention at least one of the
climate-related entities in the list, either in the parent text or the child
text, based on the assumption that even if an entity isn’t mentioned in both
parent and child text but still is the subject of the discussion. We utilized
HuggingFace’s transformers library and initialized a sentiment analysis
pipeline, this choice was motivated by the necessity to understand nuanced
emotional expressions in social media texts. We designed a function which
locates the mention of an entity and extracts a small context window around it
(30 characters before and after the entity). The sentiment within this window
is then evaluated, assigning a score and label based on the content’s
sentiment concerning the entity. If the entity is not mentioned in the text, a
neutral sentiment is automatically assigned. This approach ensures that the
sentiment analysis is focused and relevant to the specific topic being
discussed rather than the entire comment, which may contain multiple
sentiments.

We ended up gathering 8721 rows of comment-reply pairs with sentiment-parent
and sentiment-child scores towards each climate-related entity.

####  3.2.2 Model Architecture

The Climate subset of DEBAGREEMENT we use is inherently hierarchical and can
be modeled effectively as a graph. In our graph representation, each node
corresponds to a message in a thread of the social media interaction, with
edges representing the parent-child relationship between messages.

Each parent-child comment pair forms two nodes in our graph. Specifically, a
node corresponding to a parent comment will have the textual embedding of the
parent comment and its sentiment score; another node corresponding to a child
comment will have the textual embedding of the child comment and its sentiment
score (see Fig.1). Edges between nodes are formed based on the parent-child
relationships. A directed edge is created from the parent message to the child
message, capturing the flow of social conversations.

Each node (comment) has features based on textual embeddings and sentiment
scores. Differences in sentiment might be a straightforward indicator of
potential disagreement.

For capturing topological and contextual information, we deploy Graph
Attention Networks (GATs). Our architecture consists of two GAT layers:

  * •

The first GAT layer has 64 output channels with 8 attention heads. This layer
is responsible for capturing local structural information.

  * •

The second GAT layer further refines these features into 64 dimensions,
serving to abstract higher-level features from the graph.

Both GAT layers use dropout for regularization and the Exponential Linear Unit
(ELU) activation function for introducing non-linearity.

Figure 1: Diagram for the pipeline of the ClimateSent-GAT model

The core of ClimateSent-GAT lies in fusing graph-based features with textual
and sentiment features. This captures both the contextual information within a
thread and the semantic information of each individual message.

The concatenated feature vector is then passed through a fully connected layer
that has three output units corresponding to our classes: Disagree (Class 0),
Neutral (Class 1), and Agree (Class 2). A softmax activation is then applied
to convert the logits into class probabilities.

Given a directed graph $G=(V,E)$, where $V$ denotes the set of nodes
representing both parent and child comments in social media threads, and $E$
represents the set of edges indicating reply relationships, we construct the
graph’s adjacency matrix and node features to train the ClimateSent-GAT model.

For a given pair of nodes $i$ and $j$, the raw attention coefficient $e_{ij}$
is computed as:

$\displaystyle e_{ij}=\text{ELU}$
$\displaystyle\left(a^{T}\left[W_{1}h_{i,\text{embed}}\,\|\,W_{2}h_{i,\text{sentiment}}\right.\right.$
$\displaystyle\left.\left.\,\|\,W_{3}h_{j,\text{embed}}\,\|\,W_{4}h_{j,\text{sentiment}}\right]\right)$
(1)

Here, $W_{1},W_{2},W_{3},$ and $W_{4}$ are transformation matrices specific to
different feature subsets (textual embeddings and sentiment scores for both
parent and child comments). $a^{T}$ is a transposed learnable weight vector.
The ELU activation function ensures that the network maintains gradient flow
even when negative attention coefficients are encountered.

To normalize the attention coefficients, we use:

$\alpha_{ij}=\frac{\exp(e_{ij})}{\sum_{k\in N(i)}\exp(e_{ik})}$ (2)

Here, $\alpha_{ij}$ is the normalized attention coefficient, and $N(i)$
denotes the neighbors of node $i$.

Our model integrates multi-head attention, computed as:

$\displaystyle h^{\prime}_{i}=\Big{\|}_{k=1}^{K}\sigma$
$\displaystyle\left(\sum_{j\in
N(i)}\alpha_{ij}^{k}\left[W_{1}^{k}h_{j,\text{embed}}\right.\right.$
$\displaystyle\left.\left.\,\|\,W_{2}^{k}h_{j,\text{sentiment}}\right]\right)$
(3)

Each updated node feature $h^{\prime}_{i}$ incorporates information from $K$
different attention heads, each with their own transformed versions of the
node features. This allows for more diverse and richer representations.

####  3.2.3 Training and Evaluation

We divided the dataset into training, validation, and testing subsets using a
70-15-15 percentage split, respectively.

For reproducibility, we set up a fixed random seed of 42 for both NumPy and
PyTorch. We trained the model using the Adam optimizer with a learning rate of
$0.001$ and a weight decay of $5\times 10^{-4}$. To address class imbalance,
we also oversampled the minority class (Class 1, Neutral) to ensure a balanced
representation of classes in the training process. The class weights are
computed based on the oversampled dataset to further mitigate the imbalance
issue during model training.

Additionally, to prevent overfitting, we implemented an early stopping
mechanism. The patience for early stopping is set to 20 epochs. After
training, we evaluated the model on the test set to assess its performance.

Table 2: Comparison of Classification Metrics for Different Models Metrics | ClimateSent-GAT | GAT | BERT | RoBERTa | ClimateBERT
---|---|---|---|---|---
Class 0 - Disagree
Precision | 0.87 | 0.50 | 0.50 | 0.73 | 0.73
Recall | 0.78 | 0.33 | 0.88 | 0.56 | 0.56
F1-score | 0.82 | 0.39 | 0.64 | 0.63 | 0.63
Class 1 - Neutral
Precision | 0.65 | 0.16 | 0.51 | 0.36 | 0.32
Recall | 0.81 | 0.21 | 0.31 | 0.69 | 0.67
F1-score | 0.72 | 0.18 | 0.39 | 0.48 | 0.44
Class 2 - Agree
Precision | 0.78 | 0.36 | 0.58 | 0.60 | 0.61
Recall | 0.80 | 0.48 | 0.13 | 0.57 | 0.54
F1-score | 0.79 | 0.41 | 0.21 | 0.58 | 0.57
Overall Metrics
Macro Avg Precision | 0.76 | 0.34 | 0.53 | 0.56 | 0.56
Macro Avg Recall | 0.80 | 0.34 | 0.44 | 0.60 | 0.59
Macro Avg F1-score | 0.78 | 0.33 | 0.41 | 0.56 | 0.55
Weighted Avg Precision | 0.80 | 0.39 | 0.53 | 0.63 | 0.63
Weighted Avg Recall | 0.79 | 0.36 | 0.51 | 0.58 | 0.57
Weighted Avg F1-score | 0.80 | 0.37 | 0.44 | 0.59 | 0.58
Accuracy | 0.79 | 0.36 | 0.51 | 0.58 | 0.57

The experiments in this research aim to predict labels for social media
interactions through the proposed ClimateSent-GAT model, as showed in Table 2.
For comparative purposes, we also run the standalone GAT model without any
textual embeddings. For baseline models, we choose BERT uncased and RoBERTa
base models. These are reported in Pougué-Biyong et al. (2021) as yielding F1
scores of 64.2% and 63.3%, respectively, on the same classification task when
averaged across all five DEBAGREEMENT subreddits. ClimateBert Webersinke et
al. (2021) was used to define a further baseline. We mainly focus on key
metrics that best evaluate the model’s performance based on our focus on
predicting (dis)agreement among users on social media.

For the ‘Disagree‘ class, we found out that ClimateSent-GAT model achieves the
highest precision and F1-score across all models. The precision of $0.87$
suggests that the model is reliable at identifying disagreeing comment pairs
in climate-related discourse. Interestingly, ClimateSent-GAT scores higher in
recall ($0.78$), indicating that it might be more sensitive to capturing
disagreement but at the cost of more false positives, which implies that it
struggles to capture most of the disagreeing instances from the dataset. This
is significant given that identifying disagreement is critical for dialog
systems, sentiment analysis, and other NLP tasks related to social
interactions on climate change.

Secondly, for the ‘Neutral‘ class, ClimateSent-GAT model again scores the
highest in terms of precision and F1-score. Interestingly, ClimateSent-GAT
scores higher in recall ($0.79$), indicating that it might be more sensitive
to capturing neutral sentiments but at the cost of more false positives, as
evidenced by the lower precision ($0.71$).

ClimateSent-GAT surpasses all models in all metrics for the ‘Agree‘ and
’Neutral’ class. The model demonstrates its effectiveness at both accurately
identifying and capturing most of the agreeing and neutral instances.

Overall speaking, our model considerably outperforms the other models across
all overall metrics. With macro-average and weighted-average F1-scores of 0.78
and 0.80 respectively, ClimateSent-GAT sets a new state-of-the-art for
predicting disagreement between comment-reply pairs in climate change
discussions.

####  3.2.4 Improve the interpretability of existing model

ClimateSent-GAT is a hybrid model incorporates both graph and text data, and
the model is inherently complex, making it a good candidate for post-hoc
interpretability methods.

The attention mechanism of our model assigns different weights to interactions
in a graph. Thus, we first extract the attention weights from each layer of
the trained model. These attention weights are then initially averaged across
the heads for each layer to simplify the representation. Finally, we combine
the averaged attention scores from both layers to obtain a single set of
attention scores. (see Figure 2) If the model learns to associate certain
patterns of interaction (captured through embeddings and sentiment scores)
with (dis)agreement, it will assign higher attention weights to such
interactions.

Figure 2: Attention weights to the climate interactions

The x-axis in the histogram represents the attention scores, and the y-axis
represents the frequency of nodes receiving those scores. The peak and
distribution highlight our model’s focus areas, with most nodes receiving
moderate attention and a select few receiving very high attention. The
distribution shows a long tail extending towards higher attention scores.

Next, we conduct a systemtic feature ablation study to further interpret the
model. Specifically, we remove one feature at a time (e.g., sentiment scores,
textual embeddings, etc.) and observe how the model’s performance changes,
which provides an idea of which features are most important for the model.

Table 3: Feature Ablation Study Results. Notes: (1) CSGAT: ClimateSent-GAT with all features. (2) No Par Emb: Model without parent embeddings. (3) No Ch Emb: Model without child embeddings. (4) No Par Sent: Model without parent entity-based sentiment scores. (5) No Ch Sent: Model without child entity-based sentiment scores. Metric / Ablated Feature | CSGAT | No Par Emb | No Ch Emb | No Par Sent | No Ch Sent
---|---|---|---|---|---
Accuracy | 0.79 | 0.61 | 0.56 | 0.69 | 0.70
Macro Avg F1 | 0.78 | 0.59 | 0.54 | 0.67 | 0.69
Disagree F1 | 0.82 | 0.68 | 0.61 | 0.75 | 0.74
Neutral F1 | 0.72 | 0.47 | 0.48 | 0.60 | 0.63
Agree F1 | 0.79 | 0.62 | 0.54 | 0.67 | 0.68
Disagree Precision | 0.87 | 0.74 | 0.68 | 0.78 | 0.78
Neutral Precision | 0.65 | 0.40 | 0.43 | 0.55 | 0.58
Agree Precision | 0.78 | 0.61 | 0.52 | 0.66 | 0.68
Disagree Recall | 0.78 | 0.62 | 0.56 | 0.72 | 0.71
Neutral Recall | 0.81 | 0.57 | 0.54 | 0.66 | 0.71
Agree Recall | 0.80 | 0.63 | 0.56 | 0.67 | 0.68

The ablation study shows that all of our features contribute information, so
that omitting any one of them impairs performance. Replicating what Pougué-
Biyong et al. (2023) report for BERT-base classification across all five
DEBAGREEMENT subreddits, the "Without Child Embeddings" condition yields the
worst performance. The child comments are reactions to parent comments, and
appear to provide more specific, task-relevant, information about whether the
interaction is an agreement or a disagreement.

Omitting either parent entity-based sentiment or child-entity-based sentiment
impairs performance, but surprisingly, the ablated model that omits child
entity-based sentiment performs slightly better. This is counterintuitive and
might warrant further investigation. Possibly, the child’s sentiment is
somewhat redundant with other features, especially if the textual embeddings
of child comments are already rich in sentiment information.

Figure 3: Parent and Child Sentiment by Climate-Related Entities Figure 4:
Parent sentiment for entities with least vs most disagreement

In conclusion, the feature ablation studies help in understanding the
importance of different feature sets in the model. These can be interpreted as
an indication of how conversational context or sentiment may affect the
model’s ability to classify social media (dis)agreements.

The performance of ClimateSent-GAT underscores the value of incorporating both
graph attention mechanisms and robust pre-trained language models in
understanding complex social interactions on social media platforms in the
climate change discourse. It holds promise for real-world applications for
disagreement detection as well.

####  3.2.5 Climate Entities-Based Analysis

Our methodology makes it possible to identify specific factors and issues
relating to (dis)agreements in on-line discourse about climate.

To investigate how (dis)agreements are shaped around climate-related entities,
we selected the 30 most frequently occurring entities to visualize their
average entity-based sentiment scores and label distributions. Figure 3
illustrates the varying degrees of sentiment between parent and child comments
across most frequent-discussed entities, such as "Greta," "California," and
"Trump." Generally, child comments exhibit less negative sentiments compared
to their parent counterparts. This trend may suggest that child comments often
serve to counteract the tone set by parent comments.

Figure 4 compares the parent sentiment for the entities mentioned in the least
versus the most disagreements. Entities on the x-axis are sorted by
disagreement percentages in ascending order. Notably, entities involved in
more disagreements tend to have higher median parent sentiment scores. This
pattern may indicate that when a parent user refers to a climate entity in a
more positive manner, the child user often presents a contrasting opinion. We
also observed that there is no apparent correlation between sentiment
differences and levels of disagreement.

These observations are indicative of the complex interplay between the
sentiment expressed and the class of (dis)agreements in the comments. Such
dynamics are crucial for understanding how public opinions on climate issues
are shaped and propagated through social media platforms. Please see the
Appendix for a complete form of (dis)agreements distributions and entity-based
sentiment scores.

Figure 5: Average Sentiment Attention Scores for Different Entity Categories

To analyze the features or labels of nodes receiving the highest attention
scores, we selected the entities associated with the most
disagreeing/agreeing/neutral interactions, and extracted the relevant
attention weights. The most entities most associated with disagreement include
’Frank Fenner’, ’Stephen Hawking’, ’AirForce’, ’Croatia’, ’Burger King’,
’Capitol Hill’, ’the Federal Government’, ’A.O.C.’, ’great barrier reef’, and
’the Antarctic Peninsula’; the entities associated with most agreement include
’Richard Tol’, ’Jacind Adern’, ’Southern Hemisphere’, ’NDP’, ’CH4’,
’Authoritarian Communists’, ’Arthur Robinson’, ’Fred Seitz’, ’Oregon
Petition’, and ’the Lunar Lander Challenge’; the most neutral ones include
’Netherlands’, ’GHG’, ’Amazon’, ’Republican’, ’Sanders’, ’Renewables’, ’the
Twilight Zone’, ’America’, ’The Mississippi River’, and ’China’.

The differences in attention displayed in Figure 5 support our suggestion that
the ability to learn entity-specific attention weights is a factor in the
success of our model. Overall, entities associated with neutral discussions
receive the highest attention, followed by entities associated with agreement,
and finally entities associated with disagreement. These results mirror the
fact that the neutral category is the most difficult one to classify, followed
by the agreement category, followed by the disagreement category. We note that
the neutral category combines several different sorts of discourse; some
neutral posts are so-classified because they do not have any language that
expresses strong attitudes, while others have been classified as neutral
because they agree in some respects while disagreement in others. It appears
that paying attention to the specific entities under discussion helps the
model to navigate the nuances of communication in these cases.

##  4 Related Work

Several notable works precede ours in using graph-based approaches on the
DEBAGREEMENT dataset. Pougué-Biyong et al. (2023) 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 (2024)
successfully predicted disagreements in a slice of the DEBAGREEMENT 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.

##  5 Limitations

We acknowledge several limitations that future research could address.
Firstly, the reliance on data from a single social media platform (Reddit)
might limit the generalizability of the model. Social media platforms vary
significantly in user demographics and interaction styles, which can influence
discourse dynamics and the manifestation of disagreement. Secondly, the
inherent complexity of the Graph Attention Network (GAT) architecture used in
our model could pose challenges in terms of interpretability and computational
demands, which may limit deployment when scaling up the model. Lastly, while
we have taken significant steps to address ethical considerations,
particularly concerning data privacy and the potential for misuse of
disagreement detection technologies, these remain critical ongoing concerns.
Future iterations of this research should consider multi-platform studies,
enhanced methods for handling linguistic nuances, and further exploration of
ethical implications in the deployment of NLP technologies in climate
discourse.

## Acknowledgements

RS is deeply grateful for the support provided by the Jardine Scholarship,
which has made this research possible. JBP was supported by the Engineering
and Physical Sciences Research Council (EP/T023333/1).

## Appendix A: Label Distribution by Climate-Related Entities

Below is the table showing the percentage distribution of labels (Agree,
Disagree, Neutral) for various climate-related entities, we manually sampled
176 entities which contain figures, geographic locations, institutions,
climate topics and agreements as they are of most importance when studying the
(dis)agreements of climate discourse on online platforms.

## Appendix A: Label Distribution by Climate-Related Entities

Table 4: Percentage distribution of agreement labels across different climate-related entities. Entity | Agree (%) | Disagree (%) | Neutral (%)
---|---|---|---
A.O.C. | 100 | 0 | 0
ACB | 100 | 0 | 0
Africa | 30 | 55 | 15
AirForce | 0 | 100 | 0
Al gore | 0 | 100 | 0
Alex Jones | 0 | 100 | 0
Alfred Nobel | 100 | 0 | 0
Amazon | 38.46 | 46.15 | 15.38
America | 34.02 | 51.55 | 14.43
Antarctic | 23.08 | 69.23 | 7.69
Arthur Robinson | 100 | 0 | 0
Asia | 33.33 | 41.67 | 25
Augsburg University | 100 | 0 | 0
Australia | 52.78 | 30.56 | 16.67
Authoritarian Communists | 100 | 0 | 0
Baltimore | 100 | 0 | 0
Bernie | 27.78 | 55.56 | 16.67
Biden | 27.5 | 52.5 | 20
Bill Nye | 0 | 100 | 0
Bitcoin | 14.29 | 85.71 | 0
Brexit | 100 | 0 | 0
Bruce Willis | 0 | 100 | 0
Bundesverband WindEnergie | 100 | 0 | 0
Burger King | 100 | 0 | 0
CERN | 0 | 100 | 0
CH4 | 0 | 100 | 0
California | 20 | 65 | 15
Canada | 27.27 | 45.45 | 27.27
Capitalism | 7.69 | 92.31 | 0
Capitol Hill | 0 | 100 | 0
Chevron | 0 | 0 | 100
China | 27.45 | 54.9 | 17.65
Clinton | 44.44 | 50 | 5.56
Conservative | 33.33 | 50 | 16.67
Coronavirus | 50 | 0 | 50
Croatia | 0 | 0 | 100
Cube Satellites | 0 | 100 | 0
Dem | 38.89 | 44.44 | 16.67
ESA | 0 | 100 | 0
EU | 38.46 | 38.46 | 23.08
Earth | 27.78 | 58.33 | 13.89
El-Nino | 25 | 75 | 0
Elon | 40 | 60 | 0
Environmental Genocide | 0 | 100 | 0
Europe | 31.25 | 43.75 | 25
Evangelical | 0 | 100 | 0
Exxon | 30.77 | 53.85 | 15.38
Faux News | 100 | 0 | 0
Finland | 100 | 0 | 0
Florida | 58.33 | 16.67 | 25
Frank Fenner | 0 | 100 | 0
Fred Seitz | 100 | 0 | 0
GBR | 0 | 100 | 0
GE | 33.33 | 66.67 | 0
GHG | 20 | 70 | 10
GOP | 33.33 | 45.83 | 20.83
Georgetown | 0 | 100 | 0
German | 35.71 | 50 | 14.29
Gibson | 0 | 50 | 50
Great Lakes | 50 | 0 | 50
Green New Deal | 14.29 | 57.14 | 28.57
Greenpeace | 66.67 | 33.33 | 0
Greg James | 100 | 0 | 0
Greta | 55.56 | 33.33 | 11.11
Gwynne Dyer | 0 | 0 | 100
Halifax | 100 | 0 | 0
Harvey | 25 | 75 | 0
Heartland Institute | 50 | 0 | 50
Hillary | 53.85 | 38.46 | 7.69
Holly Gillibrand | 0 | 100 | 0
Hollywood | 100 | 0 | 0
Holochain | 0 | 100 | 0
Human-Caused Climate Change | 21.43 | 71.43 | 7.14
IEA | 100 | 0 | 0
IPCC | 30.77 | 46.15 | 23.08
India | 25 | 46.88 | 28.12
Inslee | 36.36 | 45.45 | 18.18
Ireland | 40 | 20 | 40
Israel | 0 | 0 | 100
Italy | 50 | 50 | 0
Jacind Adern | 100 | 0 | 0
Japan | 55.56 | 22.22 | 22.22
Jim Inhofe | 0 | 100 | 0
Kardashev | 0 | 100 | 0
Kevin Anderson | 0 | 100 | 0
KristophMcKane | 0 | 100 | 0
LNG | 100 | 0 | 0
La Nina | 100 | 0 | 0
Leonardo DiCaprio | 100 | 0 | 0
Liberal | 0 | 66.67 | 33.33
Lithium | 0 | 50 | 50
Mark Zuckerberg | 50 | 50 | 0
Mars | 60 | 40 | 0
Max Planck | 0 | 0 | 100
McConnell | 50 | 50 | 0
McPherson | 0 | 80 | 20
Miami Beach | 100 | 0 | 0
Michael McCabe | 0 | 100 | 0
Michale Bays Armageddon | 0 | 100 | 0
Myron Ebell | 100 | 0 | 0
NAFTA | 100 | 0 | 0
NASA | 16.67 | 83.33 | 0
NATO | 100 | 0 | 0
NDP | 0 | 100 | 0
NOAA | 20 | 50 | 30
Naomi Klein | 33.33 | 33.33 | 33.33
Nature Communications | 100 | 0 | 0
Netherlands | 33.33 | 33.33 | 33.33
New Zealand | 50 | 50 | 0
North Hemisphere | 0 | 0 | 100
Norway | 60 | 40 | 0
Obama | 17.24 | 62.07 | 20.69
Ohio | 33.33 | 33.33 | 33.33
Oregon Petition | 100 | 0 | 0
PBS | 100 | 0 | 0
PURE CO2 | 0 | 0 | 100
Phoenicians | 100 | 0 | 0
Pocahontas | 0 | 100 | 0
PricewaterhouseCoopers | 0 | 0 | 100
Propaganda | 0 | 50 | 50
Renewables | 0 | 100 | 0
Republican | 33.96 | 50.94 | 15.09
Richard Tol | 0 | 100 | 0
Royal Dutch Shell | 0 | 0 | 100
Russia | 35 | 45 | 20
Sanders | 29.41 | 52.94 | 17.65
Saudi Arabia | 0 | 100 | 0
Scotland | 0 | 50 | 50
Silicon | 0 | 100 | 0
Socialism | 0 | 100 | 0
Solar | 16.67 | 66.67 | 16.67
South Korea | 100 | 0 | 0
Southern Hemisphere | 0 | 100 | 0
Stephen Hawking | 0 | 100 | 0
Switzerland | 50 | 0 | 50
The BC Liberals | 0 | 100 | 0
The Mississippi River | 100 | 0 | 0
The Paris Agreement | 0 | 0 | 100
The Relative Sea Level of the Sargasso Sea | 0 | 0 | 100
Thunberg | 66.67 | 16.67 | 16.67
Trudeau | 50 | 25 | 25
Trump | 39.64 | 43.24 | 17.12
Tucson | 50 | 0 | 50
UK | 46.67 | 40 | 13.33
United States | 56.25 | 25 | 18.75
VP Gore | 100 | 0 | 0
Warren | 25 | 66.67 | 8.33
Western Europe India | 0 | 0 | 100
White House | 25 | 50 | 25
YouTube | 57.14 | 28.57 | 14.29
arctic | 22.58 | 61.29 | 16.13
christian | 33.33 | 66.67 | 0
citizens climate lobby | 100 | 0 | 0
congress | 29.41 | 64.71 | 5.88
ecosia | 0 | 66.67 | 33.33
enviro | 35.14 | 47.97 | 16.89
fossil fuels | 23.29 | 67.12 | 9.59
global warming | 25.81 | 56.45 | 17.74
great barrier reef | 100 | 0 | 0
green new deal | 33.33 | 33.33 | 33.33
healthcare | 50 | 50 | 0
methane | 33.33 | 39.39 | 27.27
the Antarctic Peninsula | 0 | 100 | 0
the Federal Government | 0 | 100 | 0
the Free Masons | 0 | 0 | 100
the Green party | 100 | 0 | 0
the Holocene Extinction | 0 | 0 | 100
the Koch Brothers | 100 | 0 | 0
the Lunar Lander Challenge | 100 | 0 | 0
the New York Times | 100 | 0 | 0
the Oval Office | 0 | 100 | 0
the Planetary Society | 0 | 100 | 0
the Supreme Court | 100 | 0 | 0
the Twilight Zone | 100 | 0 | 0
the Washington Post | 100 | 0 | 0
zero hours | 0 | 100 | 0

## Appendix B: Sentiment Analysis of Climate-Related Entities

Below is the table presents the average parent and child sentiment scores for
various climate-related entities identified in social media discussions. The
entities are sorted by percentage of ’Disagree’ in descending order.

Table 5: Parent and child sentiment scores for climate-related entities. Entity | Parent Sentiment | Child Sentiment
---|---|---
Frank Fenner | 0.7 | 0.1375
Stephen Hawking | 0.7 | 0.1375
AirForce | 0.6604166667 | 0.19
Croatia | 0.55 | 0.1637662338
Burger King | 0.5375 | 0
Capitol Hill | 0.5 | 0.2393939394
the Federal Government | 0.4 | 0.1583333333
A.O.C. | 0.3947916667 | 0.25
great barrier reef | 0.3875 | 0.4333333333
the Antarctic Peninsula | 0.375 | 0.1
Alfred Nobel | 0.3583333333 | 0
McConnell | 0.3578125 | 0.1145833334
the Green party | 0.3272727273 | -0.225
Max Planck | 0.3095454545 | -0.2291666667
Alex Jones | 0.3 | 0
Pocahontas | 0.2888888889 | -0.002083333335
Thunberg | 0.2867063492 | 0.3514814815
Brexit | 0.2857142857 | 0.08333333333
the Planetary Society | 0.2843537415 | 0.1201388889
Cube Satellites | 0.28 | -0.08333333333
Georgetown | 0.2579166667 | 0.15
Chevron | 0.25 | 0.15
Kardashev | 0.25 | 0
Israel | 0.2380952381 | 0.25
Western Europe India | 0.2380952381 | 0.25
Gwynne Dyer | 0.2277777778 | 0.01285714286
LNG | 0.225 | -0.228125
CERN | 0.2166666667 | -0.3125
ESA | 0.2166666667 | -0.3125
NAFTA | 0.2144444444 | 0.039375
Environmental Genocide | 0.2 | 0
The Paris Agreement | 0.2 | -0.08273809524
healthcare | 0.1971064815 | 0.1478174603
Great Lakes | 0.1907061688 | 0.2111111111
ACB | 0.19 | 0.1333333333
the Supreme Court | 0.19 | 0.1333333333
Greta | 0.1892405203 | 0.232546162
PBS | 0.1891836735 | 0.5590909091
KristophMcKane | 0.1875 | 0.2333333333
Holly Gillibrand | 0.1833333333 | 0.06277056277
Mars | 0.1795833333 | 0.3049206349
Halifax | 0.1666666667 | -0.08928571429
Finland | 0.1636363637 | -0.06916666667
Warren | 0.1528736772 | 0.05767609127
Ohio | 0.1518253968 | -0.01598639456
Antarctic | 0.1477039627 | 0.07841783217
California | 0.1470361652 | 0.03901541081
Augsburg University | 0.1465909091 | 0.475
Nature Communications | 0.1465909091 | 0.475
Faux News | 0.14 | -0.1666666667
Florida | 0.1391583243 | 0.06483503596
ecosia | 0.1361342593 | -0.06572420635
IEA | 0.1315277778 | 0.1779761905
the New York Times | 0.1308001894 | -0.1958333334
Gibson | 0.1291666667 | 0.2158333333
El-Nino | 0.1276271645 | 0.04766253093
Socialism | 0.1267361111 | 0.233030303
Saudi Arabia | 0.1266067266 | 0.2
Royal Dutch Shell | 0.125 | 0.1810606061
Myron Ebell | 0.1242897727 | -0.55
Inslee | 0.1224621212 | 0.03557900433
Mark Zuckerberg | 0.1210961657 | 0.09044642859
Biden | 0.1190405318 | 0.1103702946
Switzerland | 0.1170833334 | 0.0773809524
The BC Liberals | 0.1125 | 0
Dem | 0.1110492462 | 0.1301153817
Japan | 0.1094157848 | 0.07711940837
Liberal | 0.1091550926 | -0.02612433862
Kevin Anderson | 0.1071712018 | 0.02777777778
Conservative | 0.1066633598 | 0.05432249078
Solar | 0.1064361472 | 0.05602141955
congress | 0.101569448 | 0.07718646549
Russia | 0.09845155424 | -0.06192766955
YouTube | 0.09619897959 | 0.1575633031
La Nina | 0.09444444444 | 0.475
Hillary | 0.09399343711 | 0.03209917859
methane | 0.09377795815 | 0.08348263934
Norway | 0.09333333334 | -0.036
christian | 0.09 | -0.07354497356
Baltimore | 0.08944444445 | 0.2061011904
Bernie | 0.08729056437 | 0.1133162645
citizens climate lobby | 0.08518518519 | 0.65
fossil fuels | 0.08275026586 | 0.1372720437
Europe | 0.08151242927 | 0.08970922253
arctic | 0.08057866685 | 0.08545000699
Exxon | 0.07927655678 | 0.1033248696
Australia | 0.07911194883 | 0.04762864258
GE | 0.07882689744 | 0.253131905
The Relative Sea Level of the Sargasso Sea | 0.07857142857 | 0.09761904762
United States | 0.07839781746 | 0.02161907017
EU | 0.07350434822 | 0.02837598115
Bundesverband WindEnergie | 0.07012987013 | 0.2583333333
Asia | 0.06996527777 | 0.1062872024
Canada | 0.06830349399 | 0.05283802309
enviro | 0.06743364128 | 0.07928235043
Scotland | 0.0662037037 | 0.0427412518
Elon | 0.06619444444 | 0.08861111111
Clinton | 0.06288359788 | 0.01759749779
Naomi Klein | 0.06284722223 | 0.06041666668
Earth | 0.06251891916 | 0.09536423694
Obama | 0.06153171182 | 0.05222946593
UK | 0.05744136375 | 0.0688579771
Miami Beach | 0.05648148148 | -0.3
VP Gore | 0.05648148148 | -0.3
GOP | 0.05583540014 | 0.0507129162
Trump | 0.05260003078 | 0.0890837389
Netherlands | 0.04428571429 | -0.02833333333
GHG | 0.04258547008 | 0.07759920635
Amazon | 0.04178747179 | 0.04293402112
Republican | 0.03949567035 | 0.05396672248
Sanders | 0.03760270775 | 0.1168629785
Renewables | 0.03757936509 | 0.0442770713
the Twilight Zone | 0.03666666667 | -0.0625
America | 0.03620889243 | 0.08652358673
The Mississippi River | 0.03617424242 | 0.2888888889
China | 0.03377156548 | 0.09396245761
the Washington Post | 0.03166666667 | -0.1702020202
White House | 0.03088624339 | 0.07337729978
NASA | 0.02848260096 | 0.1450578704
Coronavirus | 0.02732954546 | 0.08479166667
New Zealand | 0.02711715366 | 0.2658820347
global warming | 0.02663607786 | 0.05606813624
IPCC | 0.02529853479 | -0.01513680763
Africa | 0.02424829001 | 0.05433479368
India | 0.0215511114 | 0.1012734551
Michale Bays Armageddon | 0.02142857143 | 0.08474358974
Green New Deal | 0.0145302614 | 0.1220716089
the Koch Brothers | 0.0047222222 | 0.08125
Propaganda | 0.003571428572 | -0.02395833334
Bill Nye | 0.002324263033 | 0.01226851853
the Holocene Extinction | 0 | 0.5
zero hours | 0 | 0.3333333333
Hollywood | 0 | 0.2787878788
Phoenicians | 0 | 0.2787878788
Bruce Willis | 0 | 0.2266666667
Michael McCabe | 0 | 0.2240909091
PricewaterhouseCoopers | 0 | 0.2121212121
North Hemisphere | 0 | -0.0625
Al gore | 0 | -0.1333333334
NATO | 0 | -0.2
Holochain | 0 | -0.28125
GBR | 0 | -0.3777777778
South Korea | -4.63E-18 | 0.274702381
Lithium | -0.001666666665 | 0.1489795918
Greg James | -0.002857142857 | -0.25
Leonardo DiCaprio | -0.01111111111 | -0.1166666667
Tucson | -0.01166666667 | -0.2106060606
Human-Caused Climate Change | -0.01432539683 | 0.09102419406
Trudeau | -0.01696180555 | 0.00535714286
Italy | -0.01944444445 | -0.01
Capitalism | -0.02398203647 | 0.07151251526
Bitcoin | -0.02544075965 | 0.1215455576
Greenpeace | -0.02575757575 | 0.1690972222
German | -0.02703836342 | 0.1403067666
Heartland Institute | -0.0277777778 | 0.15
PURE CO2 | -0.0287202381 | -0.1875
green new deal | -0.03453102453 | 0.06912878788
NOAA | -0.04918741733 | 0.03659722222
Silicon | -0.05333333333 | -0.2020408163
Harvey | -0.05892857143 | 0.1897321429
McPherson | -0.06264646463 | 0.03785714286
Jim Inhofe | -0.075 | -0.15
Ireland | -0.1325 | 0.1344642857
Evangelical | -0.15 | -0.193030303
the Free Masons | -0.1833333333 | -0.125
the Oval Office | -0.1888888889 | 0.2107142857
Richard Tol | -0.225 | 0.1510416667
Jacind Adern | -0.2475 | -0.05555555556
Southern Hemisphere | -0.25 | -0.175
NDP | -0.29375 | 0.0681818182
CH4 | -0.3 | 0.5
Authoritarian Communists | -0.3333333333 | -0.1333333333
Arthur Robinson | -0.35 | 0.5
Fred Seitz | -0.35 | 0.5
Oregon Petition | -0.35 | 0.5
the Lunar Lander Challenge | -0.475 | 0.2875

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