Analysis of the potential of NLP techniques to identify climate change themes in Canadian social media textual content

Negar Shabanpour, Stephane Roche , Sehl Mellouli

Social media discussions about climate change offer valuable insights into how the public views climate issues and their willingness to engage in personal climate actions. Understanding individual climate actions is crucial because households are responsible for more than 70% of global carbon emissions, and lifestyle changes alone can reduce carbon emissions by approximately 15%. This research examines 17926 textual entries from two Canadian subreddits (r/ClimateCrisisCanada, r/AskACanadian) from January 2023 to December 2024 to identify individual activities and behaviours associated with greenhouse gas (GHG) emissions discussed in Canadian climate discourse, addressing this gap in understanding authentic climate behavior priorities. We employed BERTopic (Bidirectional Encoder Representations from Transformers for topic modelling) in three configurations: baseline class-based Term Frequency-Inverse Document Frequency (c-TF-IDF), Maximal Marginal Relevance (MMR) diversity optimization, and KeyBERT semantic enhancement. This multi-configuration approach was essential because individual climate behaviors are discussed using diverse terminology and overlapping contexts, requiring different analytical lenses to capture the full spectrum of behavioral discussions without missing nuanced distinctions. Thematic analysis revealed that Canadians predominantly discuss four key behavioural domains in climate discourse: energy management (representing the largest category with 3-6 topics depending on model configuration), transportation choices (consistently 2 topics across all models), dietary decisions (2-3 topics), and consumption patterns (1 topic focused primarily on waste management and recycling). Energy discussions proved most diverse, encompassing residential heating solutions, renewable energy adoption, and nuclear power preferences, whilst transportation maintained remarkable consistency across models with distinct themes of sustainable mobility options and vehicle technology transitions. The comparative modelling approach demonstrated that different BERTopic configurations capture complementary aspects of climate behaviour discourse, with KeyBERT's semantic enhancement providing the most detailed categorization of technical solutions. These findings provide empirical evidence for understanding Canadian individual climate action priorities, offering essential insights for targeted climate policy and communication strategies.