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Recommended by Librarians: A Computational Citation Analysis Methodology for Identifying and Examining Books Promoted in LibGuides.

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Title: Recommended by Librarians: A Computational Citation Analysis Methodology for Identifying and Examining Books Promoted in LibGuides.
Authors: Orth-Alfie, Carmen1 corthalf@ku.edu, Wolfe, Erin2 edw@ku.edu
Source: Information Technology & Libraries. Mar2024, Vol. 43 Issue 1, p1-26. 26p.
Subject Terms: *Libraries, *Content analysis, *Library science, *Librarians, *Citation analysis, *Information resources, *Books, *Bibliographical citations, *Bibliography, *Metadata, *Access to information, Serial publications, Descriptive statistics
Abstract: To study library guides, as published on Springshare's LibGuides platform, new approaches are needed to expand the scope of the research, ensure comprehensiveness of data collection, and reduce bias for content analysis. Computational methods can be utilized to conduct a nuanced and thorough evaluation that critically assesses the resources promoted in library guides. Web-based library guides are curated by librarians to provide easy access to high-quality information and resources in a variety of formats to support the research needs of their users. Recent scholarship considers library guides as valuable resources and as de facto publications, highlighting the need for critical study. In this article, the authors present a novel model for comprehensively gathering data about a specific genre of books from individual LibGuide pages and applying computational methods to explore the resultant data. Beginning with a pre-selected list of 159 books, we programmatically queried the titles using the LibGuides Community search engine. After cleaning and filtering the resultant data, we compiled a list of 20,484 book references (of which 6,212 are unique) on 1,529 LibGuide pages. By testing against inclusion and exclusion criteria to ensure relevancy, we identified a total of 281 titles relevant to our topic. To gain insights for future study, citation analysis metrics are presented to reveal patterns of frequency, co-occurrence, and bibliographic coupling of books promoted in LibGuides. This proof-of-concept could be adopted for a variety of applications, including assessment of collections, public services, critical librarianship, and other complex questions to enable a richer and more thorough understanding of the information landscape of LibGuides. [ABSTRACT FROM AUTHOR]
Copyright of Information Technology & Libraries is the property of American Library Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: Recommended by Librarians: A Computational Citation Analysis Methodology for Identifying and Examining Books Promoted in LibGuides.
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  Data: <searchLink fieldCode="AR" term="%22Orth-Alfie%2C+Carmen%22">Orth-Alfie, Carmen</searchLink><relatesTo>1</relatesTo><i> corthalf@ku.edu</i><br /><searchLink fieldCode="AR" term="%22Wolfe%2C+Erin%22">Wolfe, Erin</searchLink><relatesTo>2</relatesTo><i> edw@ku.edu</i>
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– Name: Abstract
  Label: Abstract
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  Data: To study library guides, as published on Springshare's LibGuides platform, new approaches are needed to expand the scope of the research, ensure comprehensiveness of data collection, and reduce bias for content analysis. Computational methods can be utilized to conduct a nuanced and thorough evaluation that critically assesses the resources promoted in library guides. Web-based library guides are curated by librarians to provide easy access to high-quality information and resources in a variety of formats to support the research needs of their users. Recent scholarship considers library guides as valuable resources and as de facto publications, highlighting the need for critical study. In this article, the authors present a novel model for comprehensively gathering data about a specific genre of books from individual LibGuide pages and applying computational methods to explore the resultant data. Beginning with a pre-selected list of 159 books, we programmatically queried the titles using the LibGuides Community search engine. After cleaning and filtering the resultant data, we compiled a list of 20,484 book references (of which 6,212 are unique) on 1,529 LibGuide pages. By testing against inclusion and exclusion criteria to ensure relevancy, we identified a total of 281 titles relevant to our topic. To gain insights for future study, citation analysis metrics are presented to reveal patterns of frequency, co-occurrence, and bibliographic coupling of books promoted in LibGuides. This proof-of-concept could be adopted for a variety of applications, including assessment of collections, public services, critical librarianship, and other complex questions to enable a richer and more thorough understanding of the information landscape of LibGuides. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Information Technology & Libraries is the property of American Library Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.5860/ital.v43i1.16687
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        Text: English
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      – SubjectFull: Content analysis
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      – SubjectFull: Library science
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      – SubjectFull: Citation analysis
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      – SubjectFull: Books
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      – SubjectFull: Bibliography
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      – SubjectFull: Serial publications
        Type: general
      – SubjectFull: Descriptive statistics
        Type: general
    Titles:
      – TitleFull: Recommended by Librarians: A Computational Citation Analysis Methodology for Identifying and Examining Books Promoted in LibGuides.
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              Text: Mar2024
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