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Exploring Climate-Related Anxiety Through Social Media Content

Karim El-Sharkawy, Zainab Rehman, Vikrant Kumar, Ardavan Shahrabi, Bobbie Williams

arXiv:2609.12129v1cs.SI

TL;DR

Climate-related anxiety is a psychological concern, especially among young people, and social-media data offer a way to examine its themes and emotional expression at scale. This study analyses Reddit discussions and finds that topics and community interactions both shape how climate-related conversations are expressed.

  • Problem

    Climate anxiety includes psychological distress, particularly among young people, while social-media NLP offers a way to study themes and emotional expression at scale.

    Source: §1 · pp. 1–2

  • Method

    The study presents an initial framework that combines topic modelling and emotion classification to analyse climate-related discussions on Reddit.

    Source: §6 · p. 13

  • Results

    Climate-related discussions distinguish awareness-oriented from action-oriented topics, while community interactions often reshape emotional tone through support, encouragement, and alternative perspectives.

    Source: §6 · p. 14

  • Takeaways & Limitations

    ClimateLens provides an initial foundation for future research on climate anxiety in digital spaces using large-scale NLP exploration.

    Source: §6 · p. 14

    Models may misclassify or oversimplify expressions, and the framework lacks formal fairness and robustness evaluations across demographic groups.

    Source: §5 · p. 13

Abstract

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This study explores climate-related anxiety as expressed through social media discussions on Reddit. Using natural language processing techniques, we analyse large-scale textual data to identify recurring themes, emotional patterns, and how these evolve over time. Text data was preprocessed and analysed using BERTopic for topic modelling and a transformer-based model for emotion classification across 28 categories using the RoBERTa-base GoEmotions model. Results show that climate-related discourse is structured around a small number of core themes, primarily separating action-oriented discussions like advocacy and policy from informational and reflective content. Emotional analysis reveals that negative emotions such as fear and sadness are more prominent in posts, while comments often introduce a wider range of responses, including care, encouragement, and neutral reactions. These findings suggest that online climate discourse is shaped not only by the topics being discussed, but also by how users engage with and respond to one another. This work provides an initial framework for understanding climate-related anxiety through large-scale social media analysis and highlights opportunities for improving future models, expanding platform coverage, and incorporating youth-centred perspectives.

1 Introduction

Climate anxiety is framed as a psychological dimension of climate change, particularly relevant to young people. This study uses Reddit discussions to examine recurring themes, emotional expression, interactions, and change over time.

1 Introduction

  • Climate anxiety includes distress such as fear, worry, sadness, guilt, helplessness, and uncertainty, with concern and negative emotions reported among young people.A survey of 10,000 people aged 16–25 across ten countries found widespread climate concern and substantial negative emotional responses.Source: §1 · p. 1
  • Social media offers naturally occurring climate discussions where users express concerns, exchange information, and respond to one another.These networks also facilitate climate-related information sharing, dialogue, and mobilization.Source: §1 · pp. 1–2
  • Building on computational analysis of climate-anxiety discourse, the study examines Reddit themes, emotional patterns, post-comment differences, and changes over time.The introduction cites prior analysis of more than 177,000 Weibo posts as related work.Source: §1 · p. 2
  • ClimateLens V1 establishes a foundational analytical pipeline and identifies high-level patterns using Reddit, with broader data and youth-centred analysis planned for future iterations.Future plans also include additional platforms and improved model validation.Source: §1 · p. 2

2 Literature Review

The literature frames climate anxiety as distress connected to climate change and describes social media as a setting for peer connection and climate engagement. Prior computational work motivates a joint examination of themes, emotions, temporal change, and interaction dynamics.

2 Literature Review

  • Climate anxiety or eco-anxiety is defined as distress arising from climate change and its effects on everyday life, with surveys reporting substantial concern among young people.A multi-country survey reported that 75% found the future frightening and more than half felt negatively, including sadness, guilt, or worry.Source: §2 · p. 2
  • Social media can connect young people with like-minded peers and resources for environmental engagement, offering a space for communication about potentially isolating distress.The passage links these possibilities to collective climate challenges and limited trust in government responses.Source: §2 · p. 2
  • Sentiment and emotion analysis and topic modelling offer complementary ways to study climate discourse, but text complexity and method choice pose challenges.The review notes issues such as sarcasm, profanity, stopwords, and multilingual text, and contrasts LDA with BERTopic for short social-media texts.Source: §2 · p. 2
  • The paper addresses a gap in jointly examining thematic structure, fine-grained emotion, temporal evolution, and interaction dynamics across online climate discussions.Its Reddit analysis focuses particularly on differences between original posts and community responses.Source: §2 · p. 3

3 Methodology

The methodology combines keyword filtering and text cleaning with BERTopic-based thematic analysis, temporal analysis, and transformer-based emotion classification. It also uses topic and emotion-conditioned representations to examine communities and how discussions evolve.

3.1 Preprocessing and Cleaning

  • A 27-term pre-cleaning filter prioritized recall over precision by retaining records matching climate- and distress-related phrases.Cleaning preserved negations and modality and excluded documents shorter than three words.Source: §3.1 · p. 3

3.2 Topic modelling

  • BERTopic was selected to capture semantic relationships in short informal Reddit text, using transformer embeddings, UMAP, and HDBSCAN for clustering.The clustering setup used a minimum cluster size of 70.Source: §3.2 · p. 3

3.3 Topic Representation and Reduction

  • Topics were reduced to 30, while lightweight document annotations retained topic probabilities and representative-document flags for downstream analysis.The design avoids unnecessary metadata duplication while preserving interpretability.Source: §3.3 · p. 4

3.4 Topic Evolution

  • Topic evolution was smoothed with BERTopic temporal tuning and measured in monthly bins, with the number of bins bounded between 10 and 50.The bin count was dynamically determined from the dataset duration.Source: §3.4 · p. 4

3.5 Emotion Analysis

  • The RoBERTa-Base GoEmotions model predicts 28 categories; the highest-confidence label is primary, with the top three scores retained for supplementary analysis.Source: §3.5 · p. 4

3.6 Visualisations

  • Visualisations include emotion-specific frequency word clouds, monthly emotion proportions and counts, and topic-prevalence time series.The time-series views support exploration of emotional trends and emerging, stable, or declining themes.Source: §3.6 · pp. 4–5

3.7 Keyword and Community Analysis

  • Monthly emotion distributions and emotion-conditioned term clouds support comparisons of community language and temporal shifts as a proxy for community evolution.Emotion labels connect language patterns with affective categories, while temporal trends provide a proxy for thematic community dynamics.Source: §3.7 · p. 5
  • Semantically related documents form communities; 30 reduced core topics and 3–5-gram descriptors support interpretation, with representative documents anchoring each community.Representative documents are flagged by whether their cleaned text appears in the topic’s representative-document list.Source: §3.7 · p. 5

4 Results & Discussion

Reddit climate discourse clusters into action-oriented and information/reflection themes, while emotional expression varies across posts, comments, and communities.

4.1 Topic modelling Results

  • Topics cluster into distinct action/engagement and information/reflection groups, and awareness-oriented and action-oriented discussions often remain separate.Topics within each cluster share vocabulary and framing, while topics across clusters are more distinct.Source: §4.1 · p. 6
  • Climate-related discussions fluctuated over time; Topic 0, covering policy, advocacy, finance, and environmental organisations, reached its highest prevalence peak around July 2019, while Topic 2 showed increased conservation and reforestation discussion around October 2020.Source: §4.1 · pp. 6–7
  • Climate-focused posts often explain climate issues, whereas comments add urgency, advocacy, and frustration; r/gettingoverit shows comparatively constructive, supportive replies.The paper also describes depression-related posts as expressing uncertainty and hopelessness, while comments range from support to broader discussion.Source: §4.1 · pp. 7–8

4.2 Emotion Patterns

  • Across the corpus, posts more often carry negative emotions while comments broaden responses; profiles range from neutral climateoffensive and teenagers discussions to anxiety-related fear and depression-related sadness, alongside supportive gettingoverit comments.Comments may introduce care, reassurance, curiosity, encouragement, or neutral reactions.Source: §4.2 · pp. 8–10

4.3 Word-Level Emotional Signals

  • Word clouds group vocabulary by predicted emotion; the authors suggest fear, sadness, and disappointment may be distress-related, while support and collective-action language may signal resilience and hope.The visualizations aggregate Reddit documents by predicted emotion label and show commonly associated vocabulary.Source: §4.3 · pp. 11–12, Figure 14 · p. 12

4.4 Overall Implications

  • The authors characterize climate anxiety as both emotional and social, with online communities potentially raising risk awareness while also offering support and opportunities for collective engagement.They point to strengthening resilience, social support networks, and meaningful pathways for climate action.Source: §4.4 · pp. 12–13

5 Limitations & Future Work

The study’s conclusions are bounded by its Reddit data, unvalidated models, and observational design. Future work aims to validate and broaden the framework, including through youth-centred research.

Limitations

  • The analysis covers English-language Reddit discussions, which represent a subset of social media users rather than the broader youth population.Reddit’s longer-form interactions differ from platforms such as TikTok, Instagram, and Discord, where many younger users engage.Source: §5 · p. 13
  • Because the data are observational, the findings describe online discussion patterns and do not establish causal links between climate content and psychological outcomes.Source: §5 · p. 13
  • BERTopic’s topic structures depend on embeddings and clustering and dimensionality-reduction settings, so identified topics are approximate groupings rather than definitive categories.Different UMAP and HDBSCAN configurations may produce different structures.Source: §5 · p. 13
  • Sarcasm, slang, short posts, evolving youth language, and subtle or mixed emotions may lead topic and emotion models to misclassify or oversimplify content.Differences between model training data and climate discussions may also reduce performance; fairness and robustness across demographic groups and platforms remain unevaluated.Source: §5 · p. 13
  • The analysis does not capture offline coping, help-seeking, or climate action, and the bounded dataset may not represent longer-term shifts in climate anxiety.The absence of direct youth engagement also limits validation of interpretations.Source: §5 · p. 13
  • The study lacks manual annotation and ground-truth validation, so model accuracy is not formally evaluated and some patterns may reflect model bias or error.Manual annotation and benchmark evaluation are planned for future framework versions.Source: §5 · p. 13

Future Work

  • Future work will jointly model topics and emotions, develop a validated youth-focused climate-anxiety taxonomy, and assess fairness and robustness while potentially expanding platforms and languages.Planned work also includes identifying youth-written content, strengthening ethical governance through youth engagement, and improving the ClimateLens application and documentation.Source: §5 · p. 13

6 Conclusion

ClimateLens combines topic modelling and emotion classification to examine Reddit climate discussions across communities, interactions, and time. The analyses distinguish awareness- and action-oriented themes and show that interactions can broaden emotional tone beyond fear or sadness, establishing an initial framework with future work identified in model validation, youth-centred analysis, and platform and language coverage.

6 Conclusion

  • ClimateLens combines topic modelling and emotion classification to examine recurring Reddit discussion patterns, emotional variation across communities and interactions, and changes over time.Source: §6 · p. 13
  • Climate-related discussions separate into awareness-oriented and action-oriented themes, while community interactions introduce support, encouragement, and alternative perspectives beyond fear or sadness.The authors characterize climate anxiety as both an emotional and social phenomenon shaped by topics and online engagement.Source: §6 · p. 14
  • The initial framework establishes a foundation for future work on model validation, youth-centred analysis, additional platforms and languages, fairness, robustness, and ethical evaluation.The authors also describe ClimateLens as a reproducible framework and practical tool for understanding online communities' climate-anxiety experiences and responses.Source: §6 · p. 14

8 Declarations

The study used publicly available Reddit content, removed direct identifiers, and reported results in aggregate form. Raw data cannot be redistributed, but collection procedures are documented, code is publicly available, and the authors declare no competing interests.

8 Declarations

  • Public Reddit content was collected under platform terms and policies; identifiers were removed and findings are reported only in aggregate.The study used no private, deleted, or access-restricted content.Source: §8 · p. 14
  • Raw Reddit data cannot be redistributed under platform policies, but documented procedures and filtering criteria support replication by researchers with appropriate API access.Source: §8 · p. 14
  • The data collection, preprocessing, modelling, classification, and visualisation code is publicly available with documentation and execution resources.Source: §8 · p. 14
  • The authors declare no competing interests and describe individual contributions spanning methodology, technical development, analysis, writing, and design.Source: §8 · pp. 14–15

9 Appendices and Supplementary Materials

The supplementary archive provides documentation and outputs for exploring the ClimateLens pipeline and its analyses. The preprint's reported analyses use Reddit only, although some materials anticipate future Reddit and Twitter/X versions.

9 Appendices and Supplementary Materials

  • The supplementary archive includes a processed-data schema, preprocessing details, and summary statistics.Source: §9 · p. 15
  • Supplementary outputs include emotion distributions and word clouds, emotion time series, full topic representations, and dynamic topic-evolution results.Source: §9 · p. 15
  • The archive also documents model comparisons, custom word lists, pipeline parameters, and code examples; some materials concern future Reddit and Twitter/X versions, while this preprint analyses Reddit only.Source: §9 · p. 15
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