A novel method for depicting academic disciplines through Google Scholar Citations: The case of Bibliometrics

TL;DR

This paper introduces MADAP, a new method leveraging Google Scholar Citations to visualize and analyze the structure and outputs of academic communities, demonstrated through bibliometrics research.

Contribution

The paper presents MADAP, a novel approach for mapping scientific disciplines using Google Scholar data, enabling comprehensive community and publication analysis.

Findings

MADAP accurately depicts bibliometric research communities.

Google Scholar's coverage enhances discipline analysis.

The method reveals publication habits and key venues.

Abstract

This article describes a procedure to generate a snapshot of the structure of a specific scientific community and their outputs based on the information available in Google Scholar Citations (GSC). We call this method MADAP (Multifaceted Analysis of Disciplines through Academic Profiles). The international community of researchers working in Bibliometrics, Scientometrics, Informetrics, Webometrics, and Altmetrics was selected as a case study. The records of the top 1,000 most cited documents by these authors according to GSC were manually processed to fill any missing information and deduplicate fields like the journal titles and book publishers. The results suggest that it is feasible to use GSC and the MADAP method to produce an accurate depiction of the community of researchers working in Bibliometrics (both specialists and occasional researchers) and their publication habits (main publication venues such as journals and book publishers). Additionally, the wide document coverage of Google Scholar (specially books and book chapters) enables more comprehensive analyses of the documents published in a specific discipline than were previously possible with other citation indexes, finally shedding light on what until now had been a blind spot in most citation analyses.