Bibliographic Coupling Using Bibliometric Software

Introduction

• Overview of bibliographic coupling using bibliometric software.
• Earlier videos briefly covered bibliographic coupling, citation analyses, and the use of biblioshiny.

Key Points

• Bibliographic Coupling vs. Co-Citation:
  • Bibliographic coupling is preferred for clustering articles and identifying themes.
  • Biblioshiny lacks a bibliographic coupling option but offers co-citation and social network analyses.

Steps for Bibliographic Coupling Analysis

1. Library Setup:

   • Use the library command to load the necessary package in R.
   • Ensure the package is installed beforehand.

2. Data Preparation:

   • Load and convert data into the required format for analysis.
   • Example data: 279 articles from ISI in plain text format.

3. Basic Analysis:

   • Execute basic bibliometric commands to retrieve information (not the focus here).

4. Creating Network Matrix:

   • Use the bibliometric command to create a network matrix for bibliographic coupling.
     • Command Structure: bibliometric(data, analysis = "coupling", network = "references")
   • Example command details:
     • plot type = "MDS" (Multi-Dimensional Scaling)
     • Clustering algorithm: walktrap
     • Number of articles: N = 250
     • Label settings for visibility.

Visualization

• Plotting the Network:
  • Adjust label sizes and visibility for better clarity.
  • Explore options for exporting figures (e.g., save as PDF).

Exploring Other Options

• Layout Types:

  • Options for layout include: Auto, Circle, Sphere, MDS, Fruchterman, etc.
  • Example: Changing layout to Circle results in a circular plot.

• Normalization and Clustering Algorithms:

  • Normalization algorithms: Association, Jaccard, Inclusion, etc.
  • Clustering algorithms: Le'Veon, HPT witness, etc.
  • Experiment with these settings to optimize cluster appearance and meaning.

Conclusion

• Acknowledge the versatility of bibliometric tools for analysis.
• Encourage experimentation with different parameters for better results.
• Final note: A file will be uploaded for further use, and viewers are encouraged to reach out with questions.

This summary encapsulates the process and considerations for conducting bibliographic coupling using R, highlighting the importance of customization and exploration in bibliometric analyses.