Global mapping of artificial intelligence in Google and Google Scholar

Omar M., Mehmood  A., Choi  G.S., Park, H.W. (2017).
Global mapping of artificial intelligence in Google and Google Scholar. 
Scientometrics, in press.
https://doi.org/10.1007/s11192-017-2534-4

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.

Evolution of profiles of Consejo Superior de Investigaciones Científicas in Academia.edu, Google Scholar Citations and ResearchGate (2014-2015)

Ortega, JL (2017)
Toward a homogenization of academic social sites: A longitudinal study of profiles in Academia.edu, Google Scholar Citations and ResearchGate
Online Information Review, 41(6): 812-825
https://doi.org/10.1108/OIR-01-2016-0012 

Purpose

The purpose of this paper is to analyze the distribution of profiles from academic social networking sites according to disciplines, academic statuses and gender, and detect possible biases with regard to the real staff distribution. In this way, it intends to know whether these academic places tend to become specialized sites or, on the contrary, there is a homogenization process.

Design/methodology/approach

To this purpose, the evolution of profiles of one organization (Consejo Superior de Investigaciones Científicas) in three major academic social sites (Academia.edu, Google Scholar Citations and ResearchGate) through six quarterly samples since April 2014 to September 2015 are tracked.

Findings

Longitudinal results show important disciplinary biases but with strong increase of new profiles form different areas. They also suggest that these virtual spaces are gaining more stability and they tend toward a equilibrate environment.

Originality/value

This is the first longitudinal study of profiles from three major academic social networking sites and it allows to shed light on the future of these platforms’ populations.

The lost academic home: institutional affiliation links in Google Scholar Citations

Orduña-Malea, E., Ayllón, J. M., Martín-Martín, A., 
Delgado López-Cózar, E. (2017)
The lost academic home: institutional affiliation links in Google Scholar Citations. 
Online Information Review, 41 (6), 762-781
 https://doi.org/10.1108/OIR-10-2016-0302

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Purpose

Google Scholar Citations (GSC) provides an institutional affiliation link which groups together authors who belong to the same institution. The purpose of this paper is to ascertain whether this feature is able to identify and normalize all the institutions entered by the authors, and whether it is able to assign all researchers to their own institution correctly.

Design/methodology/approach

Systematic queries to GSC’s internal search box were performed under two different forms (institution name and institutional e-mail web domain) in September 2015. The whole Spanish academic system (82 institutions) was used as a test. Additionally, specific searches to companies (Google) and world-class universities were performed to identify and classify potential errors in the functioning of the feature.

Findings

Although the affiliation tool works well for most institutions, it is unable to detect all existing institutions in the database, and it is not always able to create a unique standardized entry for each institution. Additionally, it also fails to group all the authors who belong to the same institution. A wide variety of errors have been identified and classified.

Research limitations/implications

Even though the analyzed sample is good enough to empirically answer the research questions initially proposed, a more comprehensive study should be performed to calibrate the real volume of the errors.

Practical implications

The discovered affiliation link errors prevent institutions from being able to access the profiles of all their respective authors using the institutions lists offered by GSC. Additionally, it introduces a shortcoming in the navigation features of Google Scholar which may impair web user experience.

Social implications

Some institutions (mainly universities) are under-represented in the affiliation feature provided by GSC. This fact might jeopardize the visibility of institutions as well as the use of this feature in bibliometric or webometric analyses.

Originality/value

This work proves inconsistencies in the affiliation feature provided by GSC. A whole national university system is systematically analyzed and several queries have been used to reveal errors in its functioning. The completeness of the errors identified and the empirical data examined are the most exhaustive to date regarding this topic. Finally, some recommendations about how to correctly fill in the affiliation data (both for authors and institutions) and how to improve this feature are provided as well.