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Identification of future signal based on the quantitative and qualitative text mining: a case study on ethical issues in artificial intelligence

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  • Young-Joo Lee

    (National Information Society Agency (NIA))

  • Ji-Young Park

    (National Information Society Agency (NIA))

Abstract

To foresee the advent of new technologies and their socio-economic impact is a necessity for academia, governments and private enterprises as well. In the future studies, the identification of future signal is one of the renowned techniques for analysis of trends, emerging issue, and gaining future insights. In the Big Data era, recent scholars have proposed using a text mining procedure focusing upon web data such as new social media and academic papers. However, the detection of future signals is still under a developing area of research, and there is much to improve existing methodology as well as developing theoretical foundations. The present study reviews previous literature on identifying emerging issue based on the weak signal detection approach. Then the authors proposed a revised framework that incorporate quantitative and qualitative text mining for assessing the strength of future signals. The authors applied the framework to the case study on the ethical issues of artificial intelligence (hereafter AI). From EBSCO host database, the authors collected text data covering the ethical issues in AI and conducted text mining analysis. Results reveal that emerging ethical issues can be classified as strong signal, weak signal, well-known but not so strong signal, and latent signal. The revised methodology will be able to provide insights for government and business stakeholders by identifying the future signals and their meanings in various fields.

Suggested Citation

  • Young-Joo Lee & Ji-Young Park, 2018. "Identification of future signal based on the quantitative and qualitative text mining: a case study on ethical issues in artificial intelligence," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(2), pages 653-667, March.
    • Handle: RePEc:spr:qualqt:v:52:y:2018:i:2:d:10.1007_s11135-017-0582-8
    DOI: 10.1007/s11135-017-0582-8
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    References listed on IDEAS

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    Cited by:

    1. Ahn, Sang-Jin & Yoon, Ho Young & Lee, Young-Joo, 2021. "Text mining as a tool for real-time technology assessment: Application to the cross-national comparative study on artificial organ technology," Technology in Society, Elsevier, vol. 66(C).
    2. Fatma Khamis Al Badi & Khawla Ali Alhosani & Fauzia Jabeen & Agata Stachowicz-Stanusch & Nazia Shehzad & Wolfgang AMANN, 2022. "Challenges of AI Adoption in the UAE Healthcare," Vision, , vol. 26(2), pages 193-207, June.
    3. Pauliina Krigsholm & Kirsikka Riekkinen, 2019. "Applying Text Mining for Identifying Future Signals of Land Administration," Land, MDPI, vol. 8(12), pages 1-15, November.
    4. Amber Geurts & Ralph Gutknecht & Philine Warnke & Arjen Goetheer & Elna Schirrmeister & Babette Bakker & Svetlana Meissner, 2022. "New perspectives for data‐supported foresight: The hybrid AI‐expert approach," Futures & Foresight Science, John Wiley & Sons, vol. 4(1), March.
    5. Angela Maria D’Uggento & Albino Biafora & Fabio Manca & Claudia Marin & Massimo Bilancia, 2023. "A text data mining approach to the study of emotions triggered by new advertising formats during the COVID-19 pandemic," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(3), pages 2303-2325, June.
    6. Kristóf Gyódi & Łukasz Nawaro & Michał Paliński & Maciej Wilamowski, 2023. "Informing policy with text mining: technological change and social challenges," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(1), pages 933-954, February.

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