Exploring Climate-Related Anxiety Through Social Media Content
Abstract
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.