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Global mapping of artificial intelligence in Google and Google Scholar

Author

Listed:
  • Muhammad Omar

    (YeungNam University
    The Islamia University of Bahawalpur)

  • Arif Mehmood

    (YeungNam University)

  • Gyu Sang Choi

    (YeungNam University)

  • Han Woo Park

    (YeungNam University)

Abstract

The worldwide presence of AI needs to be quantified. This study proposes a descriptive approach and the use of multiple methods and data. An extensive electronic corpus of books was utilized to see the worldwide drift of intellectuals’ minds toward AI and identified related terms, their future trends, and convergence. Using the best bigram proposed by Ngrams Viewer, this study explores the human mind through Google query data, linking popular regions, countries, and related topics to the concept of AI. URL datasets were collected using two popular search engines (SEs), Google and Google Scholar (GS). A URL analysis identified key entities (organizations, institutes, and countries) and their yearly trends. Top-level domains revealed the global web ecology and the annual information growth of AI in SE environments. Information gathered through one approach was fed into the other, revealing a complementary relationship. AI is popular across the globe, and has left traces in many different countries. In this field, GS dominates Google, in relation to the number of sites and domains it includes. Top results reveal the popularity of AI among professionals, artists, programmers, and researchers. The pros and cons of the approaches are also discussed. In addition, this study aims to predict the impact of AI on society, as interpreted through the lenses of well-established theories. The dominance of AI may trap society into aspiring toward an easy life, dependent on intelligent machines. Consistent policies are needed to smooth out future economic cycles in the AI field.

Suggested Citation

  • Muhammad Omar & Arif Mehmood & Gyu Sang Choi & Han Woo Park, 2017. "Global mapping of artificial intelligence in Google and Google Scholar," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(3), pages 1269-1305, December.
    • Handle: RePEc:spr:scient:v:113:y:2017:i:3:d:10.1007_s11192-017-2534-4
    DOI: 10.1007/s11192-017-2534-4
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    References listed on IDEAS

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    Cited by:

    1. Han Woo Park & Jungwon Yoon, 2019. "Structural characteristics of institutional collaboration in North Korea analyzed through domestic publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(2), pages 771-787, May.
    2. Gruetzemacher, Ross & Paradice, David & Lee, Kang Bok, 2020. "Forecasting extreme labor displacement: A survey of AI practitioners," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    3. Houcemeddine Turki & Mohamed Ali Hadj Taieb & Mohamed Ben Aouicha & Ajith Abraham, 2020. "Nature or Science: what Google Trends says," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 1367-1385, August.
    4. Hyo Chan Park & Jonghee M. Youn & Han Woo Park, 2019. "Global mapping of scientific information exchange using altmetric data," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(2), pages 935-955, March.
    5. Naveed Naeem Abbas & Tanveer Ahmed & Syed Habib Ullah Shah & Muhammad Omar & Han Woo Park, 2019. "Investigating the applications of artificial intelligence in cyber security," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 1189-1211, November.
    6. Chung Joo Chung & Han Woo Park, 2018. "Beyond data, innovation, social network, and convergence," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(2), pages 515-518, March.

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