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Decoding Nordic cities: uncovering multi-level place experiences from tweets for effective city branding
Mahidol University International College, Salaya, Nakhonpathom, Thailand.
Halmstad University, School of Business, Innovation and Sustainability.ORCID iD: 0000-0001-6363-7066
2024 (English)In: Place Branding and Public Diplomacy, ISSN 1751-8040, E-ISSN 1751-8059, Vol. 20, no 2, p. 157-179Article in journal (Refereed) Published
Abstract [en]

This research focuses on the significance of place experiences in shaping effective place branding strategies. It introduces anovel method that leverages social media data analytics to uncover and understand place experiences, addressing the challengesof capturing these elusive experiences. The study examines the 20 major cities in the Nordic region, investigating bothcommon and distinctive place experiences among them. Additionally, it explores the changes in place experiences duringthe COVID-19 pandemic, shedding light on the impact of the crisis on people’s experiences in those cities. The researchcollected a dataset of 1,943,772 tweets associated with the 20 cities from 2017 to 2022, and employed various analyticaltechniques including topic modelling, associative word analysis, and principal component analysis to analyse and visualisethe place experiences. Furthermore, the study demonstrates the practical applications of generative AI in place branding byusing Midjourney and GPT-4 to create branding materials that capture the unique experiences of a city. Overall, the researchcontributes to the understanding of place experiences, introduces a multi-level perspective, and highlights the potential ofgenerative AI in place branding. © 2023, The Author(s), under exclusive licence to Springer Nature Limited.

Place, publisher, year, edition, pages
London: Palgrave Macmillan, 2024. Vol. 20, no 2, p. 157-179
Keywords [en]
Nordic cities, City experience, Place experience, Place analytics, Place branding, Twitter, Topic modelling, Generative AI
National Category
Business Administration
Identifiers
URN: urn:nbn:se:hh:diva-51483DOI: 10.1057/s41254-023-00313-6ISI: 001051455300001Scopus ID: 2-s2.0-85168535803OAI: oai:DiVA.org:hh-51483DiVA, id: diva2:1790213
Available from: 2023-08-22 Created: 2023-08-22 Last updated: 2024-06-26Bibliographically approved

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Rauhut Kompaniets, Olga

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