| Title |
Natural language processing-based characterization of top-down communication in smart cities for enhancing citizen alignment |
| ID_Doc |
41340 |
| Authors |
Nicolas, C; Kim, J; Chi, S |
| Title |
Natural language processing-based characterization of top-down communication in smart cities for enhancing citizen alignment |
| Year |
2021 |
| Published |
|
| DOI |
10.1016/j.scs.2020.102674 |
| Abstract |
Many city governments have implemented promising smart initiatives to make cities more efficient, livable, and ecological. To harness the full potential of smart city initiatives, it is vital for policymakers to align citizens with the project objectives. This study comprehensively characterizes and classifies top-down announcements formulated by city developers into six alignment categories (i.e., smart economy, smart people, smart governance, smart mobility, smart environment, and smart living) using natural language processing. The proposed framework consists of five main processes: (1) web scraping-based extraction of announcements of four smart cities - Boston, Helsinki, Seoul, and Taipei, (2) text data preprocessing, (3) latent Dirichlet allocation-based modeling of strategic topics, (4) quantification of inter-topic similarities using Hellinger distance, and (5) comparison of top-down communication trends with real-world levels of urban performance. Through the comparative analysis of top-down communication trends and actual urban performances, the top-down discourses of smart cities were deconstructed as a reflection of wider political programs developed to enhance citizen alignment and urban performances. Furthermore, inter-topic similarities were also quantified to reflect whether communication strategies are multidisciplinary and city-tailored. In conclusion, the findings of this study can enhance our understanding and provide workable guidance for future smart city development. |
| Author Keywords |
Smart city; Top-down communication; Citizen alignment; Natural language processing; Web scraping; Topic modeling; Latent Dirichlet allocation |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) |
| EID |
WOS:000636149400007 |
| WoS Category |
Construction & Building Technology; Green & Sustainable Science & Technology; Energy & Fuels |
| Research Area |
Construction & Building Technology; Science & Technology - Other Topics; Energy & Fuels |
| PDF |
https://doi.org/10.1016/j.scs.2020.102674
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