IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i16p9310-d617806.html
   My bibliography  Save this article

AI Augmented Approach to Identify Shared Ideas from Large Format Public Consultation

Author

Listed:
  • Min-Hsien Weng

    (School of Engineering, The University of Waikato, Hamilton 3216, New Zealand)

  • Shaoqun Wu

    (School of Computing and Mathematical Sciences, The University of Waikato, Hamilton 3216, New Zealand)

  • Mark Dyer

    (School of Engineering, The University of Waikato, Hamilton 3216, New Zealand)

Abstract

Public data, contributed by citizens, stakeholders and other potentially affected parties, are becoming increasingly used to collect the shared ideas of a wider community. Having collected large quantities of text data from public consultation, the challenge is often how to interpret the dataset without resorting to lengthy time-consuming manual analysis. One approach gaining ground is the use of Natural Language Processing (NLP) technologies. Based on machine learning technology applied to analysis of human natural languages, NLP provides the opportunity to automate data analysis for large volumes of texts at a scale that would be virtually impossible to analyse manually. Using NLP toolkits, this paper presents a novel approach for identifying and visualising shared ideas from large format public consultation. The approach analyses grammatical structures of public texts to discover shared ideas from sentences comprising subject + verb + object and verb + object that express public options. In particular, the shared ideas are identified by extracting noun, verb, adjective phrases and clauses from subjects and objects, which are then categorised by urban infrastructure categories and terms. The results are visualised in a hierarchy chart and a word tree using cascade and tree views. The approach is illustrated using data collected from a public consultation exercise called “Share an Idea” undertaken in Christchurch, New Zealand, after the 2011 earthquake. The approach has the potential to upscale public participation to identify shared design values and associated qualities for a wide range of public initiatives including urban planning.

Suggested Citation

  • Min-Hsien Weng & Shaoqun Wu & Mark Dyer, 2021. "AI Augmented Approach to Identify Shared Ideas from Large Format Public Consultation," Sustainability, MDPI, vol. 13(16), pages 1-19, August.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:16:p:9310-:d:617806
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/16/9310/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/16/9310/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mark Dyer & Min-Hsien Weng & Shaoqun Wu & Tomas García Ferrari & Rachel Dyer, 2020. "Urban Narrative: Computational Linguistic Interpretation of Large Format Public Participation for Urban Infrastructure," Urban Planning, Cogitatio Press, vol. 5(4), pages 20-32.
    2. Panagiotopoulos, Panos & Barnett, Julie & Bigdeli, Alinaghi Ziaee & Sams, Steven, 2016. "Social media in emergency management: Twitter as a tool for communicating risks to the public," Technological Forecasting and Social Change, Elsevier, vol. 111(C), pages 86-96.
    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. Roh, Taeyeoun & Yoon, Byungun, 2023. "Discovering technology and science innovation opportunity based on sentence generation algorithm," Journal of Informetrics, Elsevier, vol. 17(2).

    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. Han, Chunjia & Yang, Mu & Piterou, Athena, 2021. "Do news media and citizens have the same agenda on COVID-19? an empirical comparison of twitter posts," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    2. Shuolin Geng & Qi Zhou & Mingjie Li & Dianxing Song & Yanjun Wen, 2021. "Spatial–temporal differences in disaster perception and response among new media users and the influence factors: a case study of the Shouguang Flood in Shandong province," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 105(2), pages 2241-2262, January.
    3. Arora, Swapan Deep & Singh, Guninder Pal & Chakraborty, Anirban & Maity, Moutusy, 2022. "Polarization and social media: A systematic review and research agenda," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    4. 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.
    5. Mihalis Giannakis & Rameshwar Dubey & Shishi Yan & Konstantina Spanaki & Thanos Papadopoulos, 2022. "Social media and sensemaking patterns in new product development: demystifying the customer sentiment," Annals of Operations Research, Springer, vol. 308(1), pages 145-175, January.
    6. Patrick Cheong-Iao Pang & Qixin Cai & Wenjing Jiang & Kin Sun Chan, 2021. "Engagement of Government Social Media on Facebook during the COVID-19 Pandemic in Macao," IJERPH, MDPI, vol. 18(7), pages 1-19, March.
    7. Tracey L. O’Sullivan & Karen P. Phillips, 2019. "From SARS to pandemic influenza: the framing of high-risk populations," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 98(1), pages 103-117, August.
    8. Abhinav Khare & Qing He & Rajan Batta, 2020. "Predicting gasoline shortage during disasters using social media," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 42(3), pages 693-726, September.
    9. Nicole Olynk Widmar & Kendra Rash & Courtney Bir & Benjamin Bir & Jinho Jung, 2022. "The anatomy of natural disasters on online media: hurricanes and wildfires," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 110(2), pages 961-998, January.
    10. Grover, Purva & Kar, Arpan Kumar & Dwivedi, Yogesh K. & Janssen, Marijn, 2019. "Polarization and acculturation in US Election 2016 outcomes – Can twitter analytics predict changes in voting preferences," Technological Forecasting and Social Change, Elsevier, vol. 145(C), pages 438-460.
    11. Qingqi Long & Ke Song, 2022. "Operational Performance Evaluation of E-government Microblogs Under Emergencies Based on a DEA Method," Information Systems Frontiers, Springer, vol. 24(5), pages 1-18, October.
    12. Dominic Balog‐Way & Katherine McComas & John Besley, 2020. "The Evolving Field of Risk Communication," Risk Analysis, John Wiley & Sons, vol. 40(S1), pages 2240-2262, November.
    13. Martínez-Rojas, María & Pardo-Ferreira, María del Carmen & Rubio-Romero, Juan Carlos, 2018. "Twitter as a tool for the management and analysis of emergency situations: A systematic literature review," International Journal of Information Management, Elsevier, vol. 43(C), pages 196-208.
    14. Chiara Certomà & Mark Dyer & Antonella Passani, 2020. "The City of Digital Social Innovators," Urban Planning, Cogitatio Press, vol. 5(4), pages 1-7.
    15. May O. Lwin & Jiahui Lu & Anita Sheldenkar & Peter J. Schulz, 2018. "Strategic Uses of Facebook in Zika Outbreak Communication: Implications for the Crisis and Emergency Risk Communication Model," IJERPH, MDPI, vol. 15(9), pages 1-19, September.
    16. Al-Omoush, Khaled Saleh & Garrido, Rubén & Cañero, Julio, 2023. "The impact of government use of social media and social media contradictions on trust in government and citizens’ attitudes in times of crisis," Journal of Business Research, Elsevier, vol. 159(C).
    17. Mingyun Gu & Haixiang Guo & Jun Zhuang & Yufei Du & Lijin Qian, 2022. "Social Media User Behavior and Emotions during Crisis Events," IJERPH, MDPI, vol. 19(9), pages 1-21, April.
    18. Yi Yang & Wen Deng & Yi Zhang & Zijun Mao, 2020. "Promoting Public Engagement during the COVID-19 Crisis: How Effective Is the Wuhan Local Government’s Information Release?," IJERPH, MDPI, vol. 18(1), pages 1-17, December.
    19. De Rosis, Sabina & Lopreite, Milena & Puliga, Michelangelo & Vainieri, Milena, 2021. "The early weeks of the Italian Covid-19 outbreak: sentiment insights from a Twitter analysis," Health Policy, Elsevier, vol. 125(8), pages 987-994.
    20. Ahmad, Shmmon, 2023. "Prospects And Challenges Of E-Learning (A Review During Covid-19 Pandemic)," OSF Preprints un5g8, Center for Open Science.

    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:16:p:9310-:d:617806. 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.