Citation Analysis


Citation analysis is a bibliometric method of analysis that measures the impact of an item (typically a journal article or a book) by counting how frequently the item is cited in other articles, books and resources. It can also demonstrate patterns of use by categorizing the platforms or journals in which the citation appears. Citation analysis is most commonly used for scholarly objects such as journal articles and the same processes can also be applied to non-scholarly digital objects, such as images or primary source documents. However, the success rate is often limited due to a lack of standardized citation formats and practices for referencing non-scholarly digital objects. Likewise, the idea of truly measuring “impact” has been debated due to widely documented concerns over citation pollution and abundant self-citations within scholarly publishing. Despite these debates, it remains the most widely accepted and traditional form of measurement in assessing scholarly impact.

Applications for assessing digital content use/reuse

Citation analysis relies on how a citation is formatted and where a citation occurs (such as a works cited or reference section). For scholarly digital objects, this allows scholarship aggregators — such as Web of Science, Scopus, and Google Scholar — to regularly parse the works cited or references lists of scholarly works and update the number of times an article is cited, along with their authors and the journals in which they appear.  

Citation managers such as Mendeley can also be used to demonstrate the impact of digital objects in the future. These tools track the number of times that users collect  articles or other digital objects in their personal account libraries. While these digital objects are not yet included in the references of a published scholarly work, they indicate utility to a research process and the potential inclusion in a future publication.

For non-scholarly digital objects included in scholarly works, citation analysis is often a manual process where practitioners search scholarship aggregators (Google Scholar) or citation managers (Mendeley) for mention of specific digital objects. Likewise, searching general web indexes such as Google or Bing can help to find non-scholarly use of a digital object in web pages, blogs, or social media.


Ethical guidelines

Practitioners should follow the practices laid out in the “Ethical considerations and guidelines for the assessment of use and reuse of digital content.” The Guidelines are meant both to inform practitioners in their decision-making, and to model for users what they can expect from those who steward digital collections.

Additional guidelines for responsible practice

Many of the citation tools available for citation analysis require personal accounts, including Google, Mendeley (Elsevier), and Altmetric (Elsevier), which are known to mine a significant portion of private information from their users. It is recommended users review the privacy policies for each tool (or their parent company) to understand the information that will be collected, and how it might be used, before making a decision.


  • Citation analysis tools provide practitioners with information about the patterns of use and reuse, most specifically about how and where they are used within scholarly publications.

  • Standardized citation formats and practices allow citation analysis processes to be fully or partially automated and even outsourced to third party vendor solutions. This automation is most commonly found in the assessment of scholarly publications and platforms.

  • Unique identifiers such as DOIs (digital object identifiers), ARKs (archival resource identifiers), or URLs (uniform resource locators) help to make the process of locating the use or reuse of digital objects easier and more precise when used correctly.

  • Once an object’s use/reuse is identified, practitioners can explore the meaning and/or significance of the digital object to the user by analyzing the context in which it appears.

  • Citation analysis can also be paired with other assessment methods, such as alert services, to provide real time data on usage and impact, or reverse image search to find digital objects without accompanying citations or textual identification.


  • For non-scholarly use of works, citation analysis may require substantial time and/or personnel investment to accomplish and may not give the same return on investment as other methods. The citation analysis process is often manual and includes searching scholarship aggregators (such as Google Scholar) or the web in general for reference to specific digital objects.

  • Citation analysis is less effective for assessing non-scholarly usage because of inconsistent citation practices among non-scholarly venues such as blogs, web pages, or social media.

  • Within scholarly publications, digital objects may not be clearly cited with a standardized format or in a predictable, clearly marked location.

    • For example, some images may be included as illustrative works within a text and only noted in the caption for the image but not in the works cited or reference list. Captions may or may not include all citation information needed to identify the object (such as title, author, institution or unique identifier).

Learn how practitioners have used this method

  • Tracking research data reuse to evaluate the impact of scientific research data.
    The article makes use of the citation analysis technique to track reuse and measure the impact of publicly accessible datasets in scholarly publications through disciplinary reach, which is the number of unique journals and related subject categorizations in which articles are published.

    Chao, T. C. (2011). Disciplinary reach: Investigating the impact of dataset reuse in the earth sciences. Proceedings of the American Society for Information Science and Technology48(1), 1-8. 

  • Measuring reuse of data over time and across different datasets.
    The study uses citation analysis to examine gene expression microarray data to assess the patterns of data reuse over time and across different data sets, namely PubMed Central, HighWire Press, and Google Scholar.

    Piwowar, H. A., & Vision, T. J. (2013). Data reuse and the open data citation advantage. PeerJ175(1).

Additional resources

Aery, S. (2015, June 26). The Elastic Ruler: Measuring Scholarly Use of Digital CollectionsBitstreams: The Digital Collections Blog.

Aksnes, D. W., Langfeldt, L., & Wouters, P. (2019). Citations, Citation Indicators, and Research Quality: An Overview of Basic Concepts and TheoriesSAGE Open, 9(1).

He, L. & Nahar, V. (2016). Reuse of Scientific Data in Academic Publications: An Investigation of Dryad Digital RepositoryAslib Journal of Information Management, 68(4), 478–494.

He, L. & Han, Z. (2017). Do Usage Counts of Scientific Data Make Sense? An Investigation of the Dryad RepositoryLibrary Hi Tech, 35(2), 332–342.

Hughes, L. (2014). Live and Kicking: The Impact and Sustainability of Digital Collections in the Humanities. In Proceedings of the Digital Humanities Congress 2012. Studies in the Digital Humanities. Sheffield: HRI Online Publications.

Meyer, E.T. (2011). Splashes and Ripples: Synthesizing the Evidence on the Impact of Digital Resources. London: JISC.

Sinn, D., & Soares, N. (2014). Historians’ Use of Digital Archival Collections: The Web, Historical Scholarship, and Archival ResearchJournal of the Association for Information Science and Technology, 65(9), 1794–1809.

Sinn, D. (2012). Impact of Digital Archival Collections on Historical ResearchJournal of the American Society for Information Science and Technology, 63(8), 1521–37.

Thelwall, M. (2018). Early Mendeley readers correlate with later citation countsScientometrics, 115, 1231–1240.


Contributors to this page include Ali Shiri, Liz Woolcott, and Heidi Blackburn

Cite this page

Blackburn, H., Shiri, A., Woolcott, L. (2023). Citation Analysis. Digital Content Reuse Assessment Framework Toolkit (D-CRAFT); Council on Library & Information Resources.

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