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

Sandra Kumi; Richard K. Lomotey; Ralph Deters

doi:10.1109/HPCC-DSS-SmartCity-DependSys60770.2023.00141

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

Sandra Kumi
, Richard K. Lomotey
, Ralph Deters

Penn State Beaver

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

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.

Original language	English (US)
Title of host publication	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
Editors	Jinjun Chen, Laurence T. Yang
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	980-987
Number of pages	8
ISBN (Electronic)	9798350330014
DOIs	https://doi.org/10.1109/HPCC-DSS-SmartCity-DependSys60770.2023.00141
State	Published - 2023
Event	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 - Melbourne, Australia Duration: Dec 13 2023 → Dec 15 2023

Publication series

Name	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

Conference

Conference	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
Country/Territory	Australia
City	Melbourne
Period	12/13/23 → 12/15/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 11 Sustainable Cities and Communities

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Computer Networks and Communications
Information Systems
Information Systems and Management
Energy Engineering and Power Technology
Safety, Risk, Reliability and Quality
Instrumentation
Urban Studies

Access to Document

10.1109/HPCC-DSS-SmartCity-DependSys60770.2023.00141

Cite this

Kumi, S., Lomotey, R. K., & Deters, R. (2023). Integrating Machine Learning and Social Sensing in Smart City Digital Twin for Citizen Feedback. In J. Chen, & L. T. Yang (Eds.), 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 (pp. 980-987). (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). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/HPCC-DSS-SmartCity-DependSys60770.2023.00141

Kumi, Sandra ; Lomotey, Richard K. ; Deters, Ralph. / Integrating Machine Learning and Social Sensing in Smart City Digital Twin for Citizen Feedback. 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. editor / Jinjun Chen ; Laurence T. Yang. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 980-987 (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).

@inproceedings{7c2615ae4f3d4c4faa631b417062265b,

title = "Integrating Machine Learning and Social Sensing in Smart City Digital Twin for Citizen Feedback",

abstract = "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.",

author = "Sandra Kumi and Lomotey, \{Richard K.\} and Ralph Deters",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 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 ; Conference date: 13-12-2023 Through 15-12-2023",

year = "2023",

doi = "10.1109/HPCC-DSS-SmartCity-DependSys60770.2023.00141",

language = "English (US)",

series = "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",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "980--987",

editor = "Jinjun Chen and Yang, \{Laurence T.\}",

booktitle = "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",

address = "United States",

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Kumi, S, Lomotey, RK & Deters, R 2023, Integrating Machine Learning and Social Sensing in Smart City Digital Twin for Citizen Feedback. in J Chen & LT Yang (eds), 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. 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, Institute of Electrical and Electronics Engineers Inc., pp. 980-987, 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, Melbourne, Australia, 12/13/23. https://doi.org/10.1109/HPCC-DSS-SmartCity-DependSys60770.2023.00141

Integrating Machine Learning and Social Sensing in Smart City Digital Twin for Citizen Feedback. / Kumi, Sandra; Lomotey, Richard K.; Deters, Ralph.
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. ed. / Jinjun Chen; Laurence T. Yang. Institute of Electrical and Electronics Engineers Inc., 2023. p. 980-987 (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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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

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 - https://www.scopus.com/pages/publications/85189855974

UR - https://www.scopus.com/pages/publications/85189855974#tab=citedBy

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

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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 -

Kumi S, Lomotey RK, Deters R. Integrating Machine Learning and Social Sensing in Smart City Digital Twin for Citizen Feedback. In Chen J, Yang LT, editors, 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. Institute of Electrical and Electronics Engineers Inc. 2023. p. 980-987. (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). doi: 10.1109/HPCC-DSS-SmartCity-DependSys60770.2023.00141

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

Abstract

Publication series

Conference

UN SDGs

All Science Journal Classification (ASJC) codes

Access to Document

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