7. Analysis

Notes on Biblioshiny analysis

Bibliometric Software - See available tools here

Field tags listed here

Skarbez DOI: 10.1145/3134301

Other mapping tools

Article which links to these 3 tools:

Litmaps

Connected Papers

Inciteful

Lines of inquiry:

Which articles discuss presence but do not define it or measure it?
Was presence measured?
How was presence measured?
Which articles link the measurement of presence to some kind of outcome?
What challenges are described when measuring presence?
What challenges are described when defining presence?
Taxonomy - this may require full-text access
Terminology
Technology used, eg. VR Head Mounted Display (All-in-one or tethered), Desktop PC.

Biblioshiny

Load Biblioshiny and run with this text:

library(bibliometrix)

biblioshiny()

VOSviewer

Intro video :: CitNetExplorer

Macbook Air: install Intel Java even though M version is available but it doesn't work, end up with "Unable to load java runtime environment" error).

Network Analysis Techniques (33:00:00)

Layout Technique

Strongly connected items are located close together. AKA mapping and Visualisation of similarities (VOS). An alternative to the statistical method "multidimensional scaling"

Clustering

AKA community detection

Leiden algorithm performs the clustering (see image below)

Groups of closely related items

Definitions

Reference: Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975. https://doi.org/https://doi.org/10.1016/j.joi.2017.08.007
van Eck, N. J., & Waltman, L. (2018). VOSviewer Manual (Version 1.6.8)[Manual]. Leiden University. https://www.vosviewer.com/documentation/Manual_VOSviewer_1.6.8.pdf
Ma, T.-J., Lee, G.-G., Liu, J. S., Lan, R., & Weng, J.-H. (2022). Bibliographic coupling: a main path analysis from 1963 to 2020. Information Research, 27(1). https://doi.org/https://doi.org/10.47989/irpaper918

Performance Analysis

Evaluation of the research and publication performance of entities (individuals, institutions, & countries)

Detect most prolific and popular entities (St Pierre)

Bibliometric Analysis Visualisation

Citation networks

Citation count; Measures similarity between documents, authors, and journals; Includes co-citation and bibliometric coupling; e.g. author coupling, author co-citation, journal coupling, and journal co-citation; "Once the network has been built, a normalization process can be commonly performed over the relations (edges) between its nodes (vertices) using similarity measures such as Salton’s cosine, Jaccard’s coefficient, and Pearson’s correlation." Aria, 2017

Citation networrk

Incl: publications ; journals ; authors ; organisations ; countries

Nodes are publications and lines are the citations

Studies the cited documents; "Established by the authors who are citing the do

Co-citation network

Incl: publications ; journals ; authors ; organisations ; countries

Studies the cited documents; "Established by the authors who are citing the documents analysed"; Over time detects shifting paradigms and schools of thought; "prospective analysis – it is dynamic and is best performed within different time slices" - Aria, 2017, p. 691

"connects documents, authors, or journals on the basis of joint appearance in the reference list of another publication" (St Pierre, 2022, p. 4

"Document co-citation is based on the notion that two documents are cited together by another document, and that the strength of the co-citation of the two documents increases, the greater the number of documents that cite these documents together" (Kumpulainen, 2022)

"Co- citation analysis is used to identify groups of documents that share a similarity of subject, hence they could be interpreted as forming a semantic relation to each other." (Kumpulainen, 2022)

"Interpreting the contents of the document clusters and the relationships between the clusters forms an interesting area for bibliometric research." (Kumpulainen, 2022)

This article argues that "fractional counting" is the better option to select

Helpful Explanatory Video

Bibliographic coupling network

Incl: publications ; journals ; authors

Studies the citing documents; "Established by the authors of the articles in question"; Detects connections between research groups; "retrospective analysis – it does not change over time" - Aria, 2017, p. 691

"Connects documents, authors, or journals on the basis of the number of shared references" (St Pierre, 2022 p. 4)

"bibliographic coupling exists between two documents if they share a reference." (Kumpulainen, 2022)

Science Mapping

"is based on bibliographic networks and aims to create a representation of how disciplines, fields, specialties, and individual publications relate to one another. It uses bibliometric methods to identify the knowledge base and the social, intellectual, and conceptual structure of a research field." (St Pierre, 2022, p. 4)

Co-authorship network

Incl: authors ; organisations ; countries

Examines authors and their affiliations; Provides an index of social structures and collaboration networks;

Conceptual mapping

Co-word analysis

(Co-occurrence)

Keywords / Terms

Content analysis (cf citation analysis); Builds a conceptual/cognitive structure of a topic through a similarity measure that creates a semantic map; Can be applied to keywords, abstracts, or full texts; (Aria, 2017, p. 961) .

Normalisation (normalization)

"The normalized number of citations of a document equals the number of citations of the document divided by the average number of citations of all documents published in the same year and included in the data that is provided to VOSviewer. The normalization corrects for the fact that older documents have had more time to receive citations than more recent documents."

[IMPORTANT: I would like to present both data maps because I am both interested in recent papers but also to what extent older papers are influencing more recent research. I think this is relevant to discuss in my final paper, ie. that past research impacts future studies however with the rapid development of the technology I wonder at what aspects of the research are relevant and what insights cannot be extrapolated. Many claims are made about VR, we know it is impactful, however I don't know if the experience of presence is well communicated. ]

VOSviewer p. 24 footnote

Screenshot from presentation by Dr. Nees Jan van Eck (youtube)

Thesaurus for VOSviewer

Created using ChatGPT

To format a thesaurus for use in VOSviewer, you need to create a text file with a specific structure. The thesaurus is used to define term mappings, where different terms (synonyms, abbreviations, etc.) are mapped to a single preferred term.

Here’s a step-by-step guide to creating a properly formatted thesaurus file for VOSviewer:

1. **Create a Plain Text File**:

- Use a text editor like Notepad (Windows) or TextEdit (Mac) to create a new text file.

2. **Structure the File**:

- The file should be structured with each mapping on a new line.

- Each line should consist of terms separated by a pipe (|) character.

- The first term in each line is the preferred term.

- The subsequent terms in the line are the synonyms or variations that should be mapped to the preferred term.

3. **Example Format**:

```plaintext

PreferredTerm1|Synonym1|Synonym2|Synonym3

PreferredTerm2|SynonymA|SynonymB|SynonymC

```

For instance, if you want to map "AI", "Artificial Intelligence", and "Machine Learning" to the preferred term "Artificial Intelligence", your file should look like this:

```plaintext

Artificial Intelligence|AI|Machine Learning

```

Similarly, to map "COVID-19", "Coronavirus", and "SARS-CoV-2" to the preferred term "COVID-19", your file should include:

```plaintext

COVID-19|Coronavirus|SARS-CoV-2

```

4. **Save the File**:

- Save the text file with a `.txt` extension, for example, `thesaurus.txt`.

5. **Load into VOSviewer**:

- Open VOSviewer.

- When prompted to select a thesaurus file, navigate to and select your `thesaurus.txt` file.

- VOSviewer will use the mappings defined in your thesaurus file to process the data accordingly.

**Example Thesaurus File**:

```plaintext

Artificial Intelligence|AI|Machine Learning

COVID-19|Coronavirus|SARS-CoV-2

Big Data|Data Analytics|Large Data Sets

```

By following these steps and format, you will ensure that your thesaurus file is compatible with VOSviewer and that your terms are properly mapped during analysis.

Synonyms

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