IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i12p6911-d577592.html
   My bibliography  Save this article

New Governance Path through Digital Platforms and the Old Urban Planning Process in Italy

Author

Listed:
  • Cinzia Bellone

    (Engineering Sciencies Department, Guglielmo Marconi University, 00193 Rome, Italy)

  • Fabio Naselli

    (Department of Architecture, Epoka University, 1032 Tirana, Albania)

  • Fabio Andreassi

    (Engineering Sciencies Department, Guglielmo Marconi University, 00193 Rome, Italy)

Abstract

Current acceleration in digital practices, unexpected challenges in our social and spatial interactions, and sudden limitations in our physical spaces, mark unpredictable changes in our old normal. A different normal—as generated nowadays from the global pandemic 2020—is setting out, indeed, a mixed physical/virtual framework of the modification humanity is undertaking in being pushed into a new “digital age”; or better, as many scholars are saying, into the New Normal. A new normal in which the balance between physical and virtual interactions became in vantage of the second one in just one year, by increasing, at the same time, both the quantity and the quality of exchanging digital data. It is drafted a bi-dimensional enlarging that re-calls and stresses moreover the value of certain qualitative multi-data-based analyses aimed in reading the people’s common-sense to extrapolate wishes and needs within their daily lives; as the sentiment analysis applied to the urban planning processes wants to do. In synthesis, the bigger number of qualitative data coming from the web (from Socials mainly) became more affordable and more reliable (due to the new larger number of digital flows) in shaping new ways for a more effective public participation within the conventional planning process. In the pages of this article authors, through different but shared viewpoints, propose a possible answer to the topic of a new “Governance 3.0” addressing the attempt of a change of those consolidated paradigms within which the spatial dimension—in which we live and we act day by day—is shaped through planning processes consolidate over the years. Analyzing the relationship between Technocracy and Democracy, as defined by Khanna, it is argued that it is possible to realize new forecasts and to acquire a more democratic and participatory (inclusive) dimension of Governance, thanks to new digital technologies by exploring the general unconscious “feeling” of people, through anonymous data collection from Socials and similar platforms and without any direct or indirect interference with it. The Sentiment Analysis can “define automatic tools able to extract subjective information from texts in natural languages, such as opinions and sentiments, in order to create structured and actionable knowledge to be used by either a decision-support system or a decision-maker.

Suggested Citation

  • Cinzia Bellone & Fabio Naselli & Fabio Andreassi, 2021. "New Governance Path through Digital Platforms and the Old Urban Planning Process in Italy," Sustainability, MDPI, vol. 13(12), pages 1-11, June.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:12:p:6911-:d:577592
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/12/6911/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/12/6911/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Daniel J. Hopkins & Gary King, 2010. "A Method of Automated Nonparametric Content Analysis for Social Science," American Journal of Political Science, John Wiley & Sons, vol. 54(1), pages 229-247, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fabio Naselli & Cinzia Barbara Bellone & Mirjana Pali & Fabio Andreassi, 2022. "Tirana as an Open Lab: A Pilot for an Integrated Research Tourism Vision Pre-/Post-Pandemic," International Journal of E-Planning Research (IJEPR), IGI Global, vol. 11(1), pages 1-18, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Benjamin E. Bagozzi & Daniel Berliner & Zack W. Almquist, 2021. "When does open government shut? Predicting government responses to citizen information requests," Regulation & Governance, John Wiley & Sons, vol. 15(2), pages 280-297, April.
    2. Sinziana-Maria Rindasu & Ioan Dan Topor & Liliana Ionescu-Feleaga, 2023. "The Evolution of Management Accountants' Digital Skills in Industry 4.0: A Qualitative Approach," Oblik i finansi, Institute of Accounting and Finance, issue 1, pages 38-48, March.
    3. Giovanni Di Franco & Michele Santurro, 2021. "Machine learning, artificial neural networks and social research," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(3), pages 1007-1025, June.
    4. Zhang, Han, 2021. "How Using Machine Learning Classification as a Variable in Regression Leads to Attenuation Bias and What to Do About It," SocArXiv 453jk, Center for Open Science.
    5. Mujtaba Ali Isani, 2021. "Methodological Problems of Using Arabic-Language Twitter as a Gauge for Arab Attitudes Toward Politics and Society," Contemporary Review of the Middle East, , vol. 8(1), pages 22-35, March.
    6. Pauwels, Koen & Aksehirli, Zeynep & Lackman, Andrew, 2016. "Like the ad or the brand? Marketing stimulates different electronic word-of-mouth content to drive online and offline performance," International Journal of Research in Marketing, Elsevier, vol. 33(3), pages 639-655.
    7. Aritz Bilbao-Jayo & Aitor Almeida, 2018. "Automatic political discourse analysis with multi-scale convolutional neural networks and contextual data," International Journal of Distributed Sensor Networks, , vol. 14(11), pages 15501477188, November.
    8. Rauh, Christian, 2018. "Validating a sentiment dictionary for German political language—a workbench note," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 15(4), pages 319-343.
    9. Andrea Sciandra & Alessio Surian & Livio Finos, 2021. "Supervised Machine Learning Methods to Disclose Action and Information in “U.N. 2030 Agenda” Social Media Data," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 156(2), pages 689-699, August.
    10. Aleti, Torgeir & Pallant, Jason I. & Tuan, Annamaria & van Laer, Tom, 2019. "Tweeting with the Stars: Automated Text Analysis of the Effect of Celebrity Social Media Communications on Consumer Word of Mouth," Journal of Interactive Marketing, Elsevier, vol. 48(C), pages 17-32.
    11. Martin Haselmayer & Marcelo Jenny, 2017. "Sentiment analysis of political communication: combining a dictionary approach with crowdcoding," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(6), pages 2623-2646, November.
    12. Christopher D. Wirz & Michael A. Xenos & Dominique Brossard & Dietram Scheufele & Jennifer H. Chung & Luisa Massarani, 2018. "Rethinking Social Amplification of Risk: Social Media and Zika in Three Languages," Risk Analysis, John Wiley & Sons, vol. 38(12), pages 2599-2624, December.
    13. Tiziana CARPI & Airo HINO & Stefano Maria IACUS & Giuseppe PORRO, 2022. "A Japanese Subjective Well-Being Indicator Based on Twitter Data [‘Collective Smile: Measuring Societal Happiness from Geolocated Images’]," Social Science Japan Journal, University of Tokyo and Oxford University Press, vol. 25(2), pages 273-296.
    14. Angela Chang & Peter J. Schulz & Angus Wenghin Cheong, 2020. "Online Newspaper Framing of Non-Communicable Diseases: Comparison of Mainland China, Taiwan, Hong Kong and Macao," IJERPH, MDPI, vol. 17(15), pages 1-15, August.
    15. Francesca Greco & Alessandro Polli, 2019. "Vaccines in Italy: the emotional text mining of social media," RIEDS - Rivista Italiana di Economia, Demografia e Statistica - The Italian Journal of Economic, Demographic and Statistical Studies, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 73(1), pages 89-99, January-M.
    16. Marchand, André & Hennig-Thurau, Thorsten & Wiertz, Caroline, 2017. "Not all digital word of mouth is created equal: Understanding the respective impact of consumer reviews and microblogs on new product success," International Journal of Research in Marketing, Elsevier, vol. 34(2), pages 336-354.
    17. Lehotský, Lukáš & Černoch, Filip & Osička, Jan & Ocelík, Petr, 2019. "When climate change is missing: Media discourse on coal mining in the Czech Republic," Energy Policy, Elsevier, vol. 129(C), pages 774-786.
    18. Luigi Curini & Stefano Iacus & Luciano Canova, 2015. "Measuring Idiosyncratic Happiness Through the Analysis of Twitter: An Application to the Italian Case," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 121(2), pages 525-542, April.
    19. Christopher Kohl & Marlene Knigge & Galina Baader & Markus Böhm & Helmut Krcmar, 2018. "Anticipating acceptance of emerging technologies using twitter: the case of self-driving cars," Journal of Business Economics, Springer, vol. 88(5), pages 617-642, July.
    20. Pilar Rey del Castillo, 2022. "Building Daily Economic Sentiment Indicators," CESifo Working Paper Series 10087, CESifo.

    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:gam:jsusta:v:13:y:2021:i:12:p:6911-:d:577592. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.