TY - GEN
T1 - Integrating Machine Learning and Social Sensing in Smart City Digital Twin for Citizen Feedback
AU - Kumi, Sandra
AU - Lomotey, Richard K.
AU - Deters, Ralph
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85189855974&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85189855974&partnerID=8YFLogxK
U2 - 10.1109/HPCC-DSS-SmartCity-DependSys60770.2023.00141
DO - 10.1109/HPCC-DSS-SmartCity-DependSys60770.2023.00141
M3 - Conference contribution
AN - SCOPUS:85189855974
T3 - Proceedings - 2023 IEEE International Conference on High Performance Computing and Communications, Data Science and Systems, Smart City and Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2023
SP - 980
EP - 987
BT - Proceedings - 2023 IEEE International Conference on High Performance Computing and Communications, Data Science and Systems, Smart City and Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2023
A2 - Chen, Jinjun
A2 - Yang, Laurence T.
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on Data Science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023
Y2 - 13 December 2023 through 15 December 2023
ER -
