IDEAS home Printed from https://ideas.repec.org/a/taf/cjutxx/v32y2025i2p63-83.html
   My bibliography  Save this article

Artificial Intelligence and New Visions of the Future of the City: Exploring Urban Narratives Through Semantic and Network Analysis

Author

Listed:
  • Michela Lazzeroni
  • Antonello Romano

Abstract

This article examines the evolving perceptions of cities in the context of burgeoning artificial intelligence (AI) technologies. First, the work explores recently developed theories, such as those on urban assemblage, AI urbanism, and autonomous cities that offer useful interpretations for the recent material transformations and in conceiving cities as increasingly hybrid and complex entities. Secondly, we aim to reflect, both from a theoretical and an empirical point of view, on urban narratives that emerge around the relationship between “AI and the City.” Through the semantic and network analysis applied to a sample of data extracted from the X.com (formerly Twitter) archive, this article investigates the visions of future cities considering the opinions of people. Results reveal a spectrum of views about technological and socioeconomic aspects and narratives, which oscillate between techno-euphoria and technophobia. These findings underscore the importance of incorporating public sentiment into urban policy decisions. While AI investments can offer benefits for the functioning of urban assemblages, understanding and integrating people’s experiences and perspectives are vital for the conceptualization of new and inclusive urban models. This approach paves the way for policies that resonate more closely with the perceptions and expectations of urban dwellers in an AI-integrated future.

Suggested Citation

  • Michela Lazzeroni & Antonello Romano, 2025. "Artificial Intelligence and New Visions of the Future of the City: Exploring Urban Narratives Through Semantic and Network Analysis," Journal of Urban Technology, Taylor & Francis Journals, vol. 32(2), pages 63-83, March.
  • Handle: RePEc:taf:cjutxx:v:32:y:2025:i:2:p:63-83
    DOI: 10.1080/10630732.2025.2469326
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/10630732.2025.2469326
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/10630732.2025.2469326?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:cjutxx:v:32:y:2025:i:2:p:63-83. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/cjut20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.