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Updated: Jun 20, 2026

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

Published on: October 24, 2019

A reproducible bibliometric-content analysis workflow for mapping research fields in computer science.

Thanh-Cong Truong¹

¹Faculty of Data Science, University of Finance-Marketing, Ho Chi Minh City, Viet Nam.

|June 19, 2026

Summary

A novel workflow combines bibliometric mapping and thematic analysis for computer science research synthesis. This reproducible method, usable by a single researcher, uncovers insights beyond traditional bibliometrics.

Keywords:

Bibliometric analysis Bibliometrix Computer science Content analysis Research methodology Science mapping

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Area of Science:

Computer Science
Information Science
Bibliometrics

Background:

The exponential growth of computer science literature makes manual synthesis challenging.
Existing bibliometric methods often lack the interpretive depth for comprehensive field characterization.
Individual researchers face limitations in synthesizing large volumes of scientific publications.

Purpose of the Study:

To introduce a reproducible, two-phase workflow integrating bibliometric science mapping and thematic content analysis.
To enable comprehensive synthesis of computer science literature by individual researchers.
To identify research gaps and opportunities not apparent through bibliometric analysis alone.

Main Methods:

A two-phase workflow using R and the bibliometrix package.
Phase 1: Bibliometric science mapping (keyword co-occurrence clustering).
Phase 2: Deductive-inductive thematic coding of selected publications with a test-retest reliability procedure (κ = 0.82).

Main Results:

The workflow was applied to 648 AI-FinTech publications (2017-2026), identifying four thematic clusters.
Achieved a high inter-rater reliability score (κ = 0.82) without a second coder.
Uncovered regulatory compliance gaps and AI-blockchain integration opportunities.

Conclusions:

The integrated workflow provides a feasible and effective method for in-depth literature synthesis by individual researchers.
The test-retest procedure successfully replaces dual-coder requirements, enhancing accessibility for PhD researchers.
This approach offers a practical solution for navigating and understanding complex, rapidly growing fields within computer science.