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Architectural Institute of Korea (대한건축학회)
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How do People Understand and Express "Smart City?": Analysis of Transition in Smart-city Keywords through Semantic Network Analysis of SNS Big Data between 2011 and 2020

  • Kim, Seong-A (Department of Urban Planning and Engineering, Hanyang University) ;
  • Kim, Heungsoon (Department of Urban Planning and Engineering, Hanyang University)
  • Received : 2022.04.05
  • Accepted : 2022.06.07
  • Published : 2022.06.30
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Abstract

The purpose of this study is to grasp the understanding of smart cities and to review whether the common perception of smart cities, as people understand it, is changing over time. This study analyzes keywords related to smart cities used in social network services (SNSs) in 2011, 2016, and 2020 respectively through semantic network analysis. Smart city discussions appearing on SNS in 2011 mainly focused on technology, and the results of 2016 were generally similar to those of 2011. We can also find policy or business-oriented characteristics in emerging countries in 2020. We highlight that all the results of 2011, 2016, and 2020 have some correlation with each other through QAP(Quadratic Assignment Procedure) correlation analysis, and among them, the correlation between 2011 and 2016 is analyzed the most. The results of the frequency analysis, centrality analysis, and CONCOR(CONvergence of interaction CORrelation) analysis support these results. The results of this study help establish policies that reflect the needs and opinions of citizens in planning smart cities by identifying trends and paradigm transitions expressed by people in SNS. Furthermore, it is expected to help emerging countries by enhancing the understanding of the essence and trend of smart cities and to contribute by suggesting the direction of more sustainable technology development in future smart city policies for leading countries.

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References

  1. Aijaz. R. (2021). India's Smart Cities Mission, 2015-2021: A Stocktaking. Observer Research Foundation, Aagust 16: 1-29.
  2. Albino, V., Berardi, U., & Dangelico, R. M. (2015). Smart cities: Definitions, dimensions, performance, and initiatives. J Urban Technol, 22(1): 3-21. https://doi.org/10.1080/10630732.2014.942092
  3. Alizadeh, T., Sarkar, S., & Burgoyne, S. (2019). Capturing citizen voice online: Enabling smart participatory local government. Cities, 95, 102400. https://doi.org/10.1016/j.cities.2019.102400
  4. Alkhammash et al., E. H., Jussila, J., Lytras, M. D., & Visvizi, A. (2019). Annotation of smart cities Twitter micro-contents for enhanced citizen's engagement. IEEE, 7: 116267-116276.
  5. Alotaibi, S., Mehmood, R., & Katib, I. (2019). Sentiment analysis of Arabic tweets in smart cities: A review of Saudi dialect. IEEE, 330-335.
  6. Angelidou, M. (2017). The role of smart city characteristics in the plans of fifteen cities. Journal of Urban Technology, 24(4): 3-28. https://doi.org/10.1080/10630732.2017.1348880
  7. ASCN. (2021). Available online: https://asean.org/our-communities/asean-smart-cities-network (accessed on 29 July 2021).
  8. Batty, M., Axhausen, K. W., Giannotti, F., Pozdnoukhov, A., Bazzani, A., Wachowicz, M., & Portugali, Y. (2012). Smart cities of the future. Eur Phys J Special Topics, 214(1): 481-518. https://doi.org/10.1140/epjst/e2012-01703-3
  9. Bencke, L., Cechinel, C., & Munoz, R. (2020). Automated classification of social network messages into Smart cities dimensions. Future Generation Computer Systems, 109: 218-237. https://doi.org/10.1016/j.future.2020.03.057
  10. Bouskela, M., Casseb, M., Bassi, S., De Luca, C., & Facchina, M. (2016). The Road Toward Smart Cities. Inter-American Development Bank (IDB), 1-148.
  11. Brandt, T., Bendler, J., & Neumann, D. (2017). Social media analytics and value creation in urban smart tourism ecosystems. Inf Manag, 54(6): 703-713. https://doi.org/10.1016/j.im.2017.01.004
  12. Caragliu, A., Del Bo, C., & Nijkamp, P. (2011). Smart cities in Europe. J Urban Technol, 18(2): 65-82. https://doi.org/10.1080/10630732.2011.601117
  13. Ciuccarelli, P., Lupi, G., & Simeone, L. (2014). Reflections on potentialities and shortcomings of geo-located social media analysis. Visualizing Data City, 5-61.
  14. Clement, J., & Crutzen, N. (2021). How Local Policy Priorities Set the Smart City Agenda. Technological Forecasting and Social Change, 171: 120985. https://doi.org/10.1016/j.techfore.2021.120985
  15. Choi, B. M. (2011). A study on setting up the concept of smart city through analysis on the term 'Smart'. The Journal of the Korea Contents Association, 11(12): 943-949. https://doi.org/10.5392/JKCA.2011.11.12.943
  16. Colladon, A. F., Grassi, S., Ravazzolo, F., & Violante, F. (2020). Forecasting financial markets with semantic network analysis in the COVID-19 crisis. arXiv preprint arXiv, 2009, 04975.
  17. DataReportal. (2021). Available online: https://datareportal.com/global-digital-overview (accessed on 17 September 2021).
  18. Doran, D., Severin, K., Gokhale, S., & Dagnino, A. (2016). Social media enabled human sensing for smart cities. AI Commun, 29(1): 57-75. https://doi.org/10.3233/aic-150683
  19. Drahosova, M., & Balco, P. (2017). The analysis of advantages and disadvantages of use of social media in European Union. Procedia Comp Sci, 109: 1005-1009. https://doi.org/10.1016/j.procs.2017.05.446
  20. Drieger, P. (2013). Semantic network analysis as a method for visual text analytics. Procedia-Soc Behav Sci, 79: 4-17. https://doi.org/10.1016/j.sbspro.2013.05.053
  21. EU. (2021). Available online: https://ec.europa.eu (accessed on 29 July 2021).
  22. European Parliament. (2014). Policy Department A: Economic and Scientific Policy. Mapping Smart Cities in the EU, January.
  23. Ferrer, J. R. (2017). Barcelona's Smart City vision: an opportunity for transformation. Field Actions Science Reports. The journal of field actions, Special Issue 16: 70-75.
  24. Freeman, L. C. (2007). Social Network Analysis. SAGE: London, UK.
  25. Giffinger, R., Fertner, C., Kramar, H., & Meijers, E. (2007). City-ranking of European medium-sized cities. Cent Reg Sci Vienna UT, 1-12.
  26. GreenBiz. (2021). Available online: https://www.greenbiz.com/article/market-smart-city-technology-reach-16b-year-2020 (accessed on 29 July 2021).
  27. Harrison, C., & Donnelly, I. A. (2011). A theory of smart cities. In Proceedings of the 55th Annual Meeting of the ISSS-2011, Hull, UK, 55(1).
  28. Hubert. (1987). Assignment Methods in Combinatorial Data Analysis. Marcel Dekker: New York, US.
  29. Ikeda, K., Hattori, G., Ono, C., Asoh, H., & Higashino, T. (2013). Twitter user profiling based on text and community mining for market analysis. Knowledge-Based Syst, 51: 35-47. https://doi.org/10.1016/j.knosys.2013.06.020
  30. IMD. (2021). Available online: https://www.imd.org (accessed on 17 September 2021).
  31. MD. (2021). Available online: https:/www.imd.org/ (accessed on 17 September 2021).
  32. India Ministry of Housing and Urban Affairs. (2021). Available online: https://mohua.gov.in (accessed on 29 July 2021).
  33. Injadat, M., Salo, F., & Nassif, A. B. (2016). Data mining techniques in social media: A survey. Neurocomputing, 214: 654-670. https://doi.org/10.1016/j.neucom.2016.06.045
  34. ITU. (2021). Available online: https://www.itu.int/search (accessed on 29 July 2021).
  35. Johnson, P. A., Robinson, P. J., & Philpot, S. (2020). Type, tweet, tap, and pass: How smart city technology is creating a transactional citizen. Gov Inf Quart, 37(1): 101414. https://doi.org/10.1016/j.giq.2019.101414
  36. Kang, D. B. (2019). Comparison of unplugged activities at home and abroad using semantic network analysis. J Korean Assoc Comp Educ, 22(4): 21-34. https://doi.org/10.32431/KACE.2019.22.4.003
  37. Kankanamge, N., Yigitcanl ar, T., Go onetilleke, A., & Kamruzzaman, M. (2020). Determining disaster severity through social media analysis: Testing the methodology with South East Queensland Flood tweets. Int J Disaster Risk Reduction, 42: 101360. https://doi.org/10.1016/j.ijdrr.2019.101360
  38. Krackhardt. (1987). QAP Partialling as a Test of Spuriousness. Soc Networks, 9(2): 171-186. https://doi.org/10.1016/0378-8733(87)90012-8
  39. Kubina, M., Sulyova, D., & Vodak, J. (2021). Comparison of Smart City Standards, Implementation and Cluster Models of Cities in North America and Europe. Sustainability, 13(6): 3120. https://doi.org/10.3390/su13063120
  40. Modrek, S., & Chakalov, B. (2019). The #MeToo movement in the United States: text analysis of early twitter conversations. J Med Internet Res, 21(9): e13837. https://doi.org/10.2196/13837
  41. NDRC. (2021). Available online: https://www.ndrc.gov.cn (accessed on 29 July 2021).
  42. Nicolas, C., Kim, J., & Chi, S. (2021). Natural language processing-based characterization of top-down communication in smart cities for enhancing citizen alignment. Sustainable Cities Soc, 66: 102674. https://doi.org/10.1016/j.scs.2020.102674
  43. Obama White House. (2021). Available online: https://obamawhitehouse.archives.gov (accessed on 29 July 2021).
  44. OECD. (2020). Smart Cities and Inclusive Growth, 1-59.
  45. Oh, J. (2020). Smart city as a tool of citizen-oriented urban regeneration: Framework of preliminary evaluation and its application. Sustainability, 12(17): 6874. https://doi.org/10.3390/su12176874
  46. Osorio-Arjona, J., & Garcia-Palomares, J. C. (2019). Social media and urban mobility: Using twitter to calculate homework travel matrices. Cities, 89: 268-280. https://doi.org/10.1016/j.cities.2019.03.006
  47. Pereira, J. F. F. (2017). Social media text processing and semantic analysis for smart cities. Master's Thesis, Cornell University, New York, 27 June 2017.
  48. POSCO. (2021). Available online: https://newsroom.posco.com/en/the-evolution-of-smart-cities-and-opportunities-for-steel-industry (accessed on 29 July 2021).
  49. Sevin, H. E. (2014). Understanding cities through city brands: City branding as a social and semantic network. Cities, 38: 47-56. https://doi.org/10.1016/j.cities.2014.01.003
  50. Shim, J., Park, C., & Wilding, M. (2015). Identifying policy frames through semantic network analysis: an examination of nuclear energy policy across six countries. Pol Sci, 48(1): 51-83. https://doi.org/10.1007/s11077-015-9211-3
  51. Silva, B. N., Khan, M., & Han, K. (2018). Towards sustainable smart cities: A review of trends, architectures, components, and open challenges in smart cities. Sustainable Cities Soc, 38: 697-713. https://doi.org/10.1016/j.scs.2018.01.053
  52. SO, J., KIM, T., KIM, M., KANG, J., LEE, H., & CHOI, J. M. A. (2019). Study on the Concept of Smart city and Smart City Transport. J Korean Soc Transp, 79-91. https://doi.org/10.7470/jkst.2019.37.1.079
  53. TEXTOM. (2021). Available online: https://www.textom.co.kr (accessed on 29 July 2021).
  54. UK BIS. (2021). Available online: https://www.gov.uk (accessed on 29 July 2021).
  55. Verma, D., Jana, A., & Ramamritham, K. (2019). Machine-based understanding of manually collected visual and auditory datasets for urban perception studies. Landscape Urban Planning, 190: 103604. https://doi.org/10.1016/j.landurbplan.2019.103604
  56. Weiler A., Grossniklaus M., & Scholl M.H. (2017). Survey and experimental analysis of event detection techniques for Twitter Comput. Comp J, 60(3): 329-346. https://doi.org/10.1093/comjnl/bxw056
  57. Yigitcanlar, T., Kankanamge, N., & Vella, K. (2020). How are smart city concepts and technologies perceived and utilized? A systematic geo-twitter analysis of smart cities in Australia. J Urban Technol, 1-20. https://doi.org/10.1080/1063073032000175381