Global mapping of artificial intelligence in Google and Google Scholar

Muhammad Omar, Arif Mehmood, Gyu Sang Choi and Han Woo Park ()
Additional contact information
Muhammad Omar: YeungNam University
Arif Mehmood: YeungNam University
Gyu Sang Choi: YeungNam University
Han Woo Park: YeungNam University

Scientometrics, 2017, vol. 113, issue 3, No 4, 1269-1305

Abstract: 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.

Keywords: Artificial intelligence (AI); Search engine (SE); Google; Google Scholar (GS); URLs; Domains; Sites; Google query data; Google Books Ngram Viewer (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://link.springer.com/10.1007/s11192-017-2534-4 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:scient:v:113:y:2017:i:3:d:10.1007_s11192-017-2534-4

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11192

DOI: 10.1007/s11192-017-2534-4

Access Statistics for this article

Scientometrics is currently edited by Wolfgang Glänzel

More articles in Scientometrics from Springer, Akadémiai Kiadó
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().