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Affective computing scholarship and the rise of China: a view from 25 years of bibliometric data

Author

Listed:
  • Manh-Tung Ho

    (Ritsumeikan Asia Pacific University, Beppu
    Phenikaa University, Yen Nghia Ward
    Vietnam Academy of Social Sciences)

  • Peter Mantello

    (Ritsumeikan Asia Pacific University, Beppu)

  • Hong-Kong T. Nguyen

    (Ritsumeikan Asia Pacific University, Beppu
    Phenikaa University, Yen Nghia Ward
    AI for Social Data Lab, Vuong & Associates)

  • Quan-Hoang Vuong

    (Phenikaa University, Yen Nghia Ward
    AI for Social Data Lab, Vuong & Associates)

Abstract

Affective computing, also known as emotional artificial intelligence (AI), is an emerging and cutting-edge field of AI research. It draws on computer science, engineering, psychology, physiology, and neuroscience to computationally model, track, and classify human emotions and affective states. While the US once dominated the field in terms of research and citation from 1995–2015, China is now emerging as a global contender in research output, claiming second place for the most cited country from 2016–2020. This article maps the rhizomatic growth and development of scientific publications devoted to emotion-sensing AI technologies. It employs a bibliometric analysis that identifies major national contributors and international alliances in the field over the past 25 years. Contrary to the ongoing political rhetoric of a new Cold War, we argue that there are in fact vibrant AI research alliances and ongoing collaborations between the West and China, especially with the US, despite competing interests and ethical concerns. Our observations of historical data indicate two major collaborative networks: the “US/Asia-Pacific cluster” consisting of the US, China, Singapore, Japan and the “European” cluster of Germany, the UK, and the Netherlands. Our analysis also uncovers a major shift in the focus of affective computing research away from diagnosis and detection of mental illnesses to more commercially viable applications in smart city design. The discussion notes the state-of-the-art techniques such as the ensemble method of symbolic and sub-symbolic AI as well as the absence of Russia in the list of top countries for scientific output.

Suggested Citation

  • Manh-Tung Ho & Peter Mantello & Hong-Kong T. Nguyen & Quan-Hoang Vuong, 2021. "Affective computing scholarship and the rise of China: a view from 25 years of bibliometric data," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-14, December.
  • Handle: RePEc:pal:palcom:v:8:y:2021:i:1:d:10.1057_s41599-021-00959-8
    DOI: 10.1057/s41599-021-00959-8
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    References listed on IDEAS

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    Cited by:

    1. Ho, Manh-Tung & Mantello, Peter & Ghotbi, Nader & Nguyen, Minh-Hoang & Nguyen, Hong-Kong T. & Vuong, Quan-Hoang, 2022. "Rethinking technological acceptance in the age of emotional AI: Surveying Gen Z (Zoomer) attitudes toward non-conscious data collection," Technology in Society, Elsevier, vol. 70(C).
    2. Peter Mantello & Manh-Tung Ho & Minh-Hoang Nguyen & Quan-Hoang Vuong, 2023. "Machines that feel: behavioral determinants of attitude towards affect recognition technology—upgrading technology acceptance theory with the mindsponge model," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-16, December.
    3. Ho, Manh-Tung & Le, Ngoc-Thang B. & Mantello, Peter & Ho, Manh-Toan & Ghotbi, Nader, 2023. "Understanding the acceptance of emotional artificial intelligence in Japanese healthcare system: A cross-sectional survey of clinic visitors’ attitude," Technology in Society, Elsevier, vol. 72(C).
    4. Behn, Oliver & Leyer, Michael & Iren, Deniz, 2024. "Employees’ acceptance of AI-based emotion analytics from speech on a group level in virtual meetings," Technology in Society, Elsevier, vol. 76(C).
    5. Carlos Lamela-Orcasitas & Jesús García-Madariaga, 2023. "How to really quantify the economic value of customer information in corporate databases," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-13, December.

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