Recommended by Librarians

A Computational Citation Analysis Methodology for Identifying and Examining Books Promoted in LibGuides




LibGuides, library guides, content analysis, citation analysis, co-citation, print and electronic books, computational methods, research methodology, Python


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.

Author Biographies

Carmen Orth-Alfie, University of Kansas

Carmen Orth-Alfie is a Graduate Student Engagement and Business Librarian at the University of Kansas.  In addition to instruction and research consultations responsibilities she contributes to programming and services for graduate students, open education initiatives, and evidence synthesis research. Her research interests include information seeking behaviors and strategies.

Erin Wolfe, University of Kansas

Erin Wolfe is the Digital Initiatives Librarian at the University of Kansas, where he works with the Libraries on a variety of tasks involving the creation, description, and organization of digital materials for access and preservation, along with instruction and consultations in tools and practices for metadata management, dataset creation, digital preservation, and related topics. His research interests include computational text analysis, data mining and visualization, machine learning, and programmatically assisted metadata enhancement.


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How to Cite

Orth-Alfie, C., & Wolfe, E. (2024). Recommended by Librarians: A Computational Citation Analysis Methodology for Identifying and Examining Books Promoted in LibGuides. Information Technology and Libraries, 43(1).