Quantifying the temporal dynamics of environmental awareness through longitudinal social media analysis

Understanding how societies perceive and adapt to environmental change requires analyzing awareness as a dynamic process emerging from coupled human–environment interactions. This study presents a longitudinal, data-driven analysis of environmental awareness based on three years of social-media discourse related to air pollution. Using sentiment analysis (Valence Aware Dictionary and Sentiment Reasoner and Twitter-RoBERTa) and topic modeling (BERTopic), we quantify the temporal evolution of collective public awareness and identify dominant discourse themes. A correlation analysis between sentiment indicators and PM10 levels reveals synchronized fluctuations, while mutual information indicates that public awareness becomes increasingly dependent on or shaped by pollution variability. Furthermore, Kruskal–Wallis tests confirm statistically significant temporal variations in sentiment distributions, underscoring adaptive shifts in public awareness across years. By interpreting awareness as a measurable order parameter within a complex socio-environmental system, this work demonstrates how collective perception emerges from nonlinear feedbacks between environmental forcing and public information flow. The proposed multilayer socio-environmental perception framework integrates machine-learning-based sentiment and topic analysis with complex-systems concepts to quantify emergent awareness dynamics from longitudinal social-media data.