Integrating Machine Learning and Social Sensing in Smart City Digital Twin for Citizen Feedback

Smart City Digital Twin (SCDT), a virtual representation of a physical city, is an emerging technology for optimizing urban services and enhancing urban planning and decision-making. The integration of Machine Learning (ML) and social sensing provides valuable insights into public feedback to policymakers and for informed decision-making and responsive urban governance. This study aims to explore the use of social media data and topic modeling algorithms in the context of SCDT to highlight the concerns of citizens in Saskatoon, Canada. In this work, we use the Uniform Manifold Approximation and Projection (UMAP) and K-means clustering algorithm in the BERTopic architecture to extract 30 topics from comments collected through the Saskatoon subreddit posts. The topics were then merged into 15 themes to discover the concerns. A pretrained transformer model, SiEBERT was used to determine the sentiments of the Reddit comments. The research findings highlighted concerns such as - unreliable public transit, high cost of living, long wait times in emergency rooms, shortage of family doctors, drug addiction, and lack of mental awareness.