Improving the accuracy of co-citation clustering using full text
Abstract
Historically, co‐citation models have been based only on bibliographic information. Full‐text analysis offers the opportunity to significantly improve the quality of the signals upon which these co‐citation models are based. In this work we study the effect of reference proximity on the accuracy of co‐citation clusters. Using a corpus of 270,521 full text documents from 2007, we compare the results of traditional co‐citation clustering using only the bibliographic information to results from co‐citation clustering where proximity between reference pairs is factored into the pairwise relationships. We find that accounting for reference proximity from full text can increase the textual coherence (a measure of accuracy) of a co‐citation cluster solution by up to 30% over the traditional approach based on bibliographic information.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Another group of studies (mostly from recent years) have investigated the context of citations.
“Another group of studies (mostly from recent years) have investigated the context of citations (see, e.g., Boyack et al., 2013, Zhao and Strotmann, 2014, Jha et al., 2016).”
From Core Elements in the Process of Citing Publications: Conceptual Overview of the Literature (2018) · cites this paper as Boyack et al., 2013
Cites
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“The intent behind this work has been to understand the motivation behind and relative importance of individual citations.”
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Flagged first, then by citations
Cited by
Sorted by citations
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Self-citation
- Authors
- Boyack, K. W., Small, H., & Klavans, R.
- Affiliations
- SciTech Strategies (United States) (US)
- Corresponding author
- Boyack, Kevin W.
- Small, Henry
- Klavans, Richard
- Venue
- Journal of the American Society for Information Science and Technology
- Publisher
- Wiley
- Published
- 2013-07-19
- Volume
- 64
- Issue
- 9
- Pages
- 1759-1767
- ISSN
- 1532-2882 (ISSN-L)
- 1532-2890
- OpenAlex
- W2100774734
- Semantic Scholar
- 80a8dc309e15f54a15bd7b6c91e61c2a52abb6f0
- Cited by
- 117
- References
- 21
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 5 · 2023: 1 · 2022: 8 · 2021: 9 · 2020: 11 · 2019: 18 · 2018: 9 · 2017: 11 · 2016: 11 · 2015: 15 · 2014: 11 · 2013: 1
- FWCI
- 13.95
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of the American Society for Information Science and Technology (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- U.S. Department of the Interior
- IBM Center for the Business of Government
- Interior Business Center
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › scientometrics and bibliometrics research
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Text Analysis Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Clustering Algorithms Research
- Keywords
- full-text analysis, bibliographic information, text coherence, citation analysis, citation networks
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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