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Systematic Characterizations of Text Similarity in Full Text Biomedical Publications

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  • Zhaohui Sun
  • Mounir Errami
  • Tara Long
  • Chris Renard
  • Nishant Choradia
  • Harold Garner

Abstract

Background: Computational methods have been used to find duplicate biomedical publications in MEDLINE. Full text articles are becoming increasingly available, yet the similarities among them have not been systematically studied. Here, we quantitatively investigated the full text similarity of biomedical publications in PubMed Central. Methodology/Principal Findings: 72,011 full text articles from PubMed Central (PMC) were parsed to generate three different datasets: full texts, sections, and paragraphs. Text similarity comparisons were performed on these datasets using the text similarity algorithm eTBLAST. We measured the frequency of similar text pairs and compared it among different datasets. We found that high abstract similarity can be used to predict high full text similarity with a specificity of 20.1% (95% CI [17.3%, 23.1%]) and sensitivity of 99.999%. Abstract similarity and full text similarity have a moderate correlation (Pearson correlation coefficient: −0.423) when the similarity ratio is above 0.4. Among pairs of articles in PMC, method sections are found to be the most repetitive (frequency of similar pairs, methods: 0.029, introduction: 0.0076, results: 0.0043). In contrast, among a set of manually verified duplicate articles, results are the most repetitive sections (frequency of similar pairs, results: 0.94, methods: 0.89, introduction: 0.82). Repetition of introduction and methods sections is more likely to be committed by the same authors (odds of a highly similar pair having at least one shared author, introduction: 2.31, methods: 1.83, results: 1.03). There is also significantly more similarity in pairs of review articles than in pairs containing one review and one nonreview paper (frequency of similar pairs: 0.0167 and 0.0023, respectively). Conclusion/Significance: While quantifying abstract similarity is an effective approach for finding duplicate citations, a comprehensive full text analysis is necessary to uncover all potential duplicate citations in the scientific literature and is helpful when establishing ethical guidelines for scientific publications.

Suggested Citation

  • Zhaohui Sun & Mounir Errami & Tara Long & Chris Renard & Nishant Choradia & Harold Garner, 2010. "Systematic Characterizations of Text Similarity in Full Text Biomedical Publications," PLOS ONE, Public Library of Science, vol. 5(9), pages 1-6, September.
    • Handle: RePEc:plo:pone00:0012704
    DOI: 10.1371/journal.pone.0012704
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    References listed on IDEAS

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    1. Mounir Errami & Harold Garner, 2008. "A tale of two citations," Nature, Nature, vol. 451(7177), pages 397-399, January.
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    Cited by:

    1. Antonio García-Romero & José Manuel Estrada-Lorenzo, 2014. "A bibliometric analysis of plagiarism and self-plagiarism through Déjà vu," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(1), pages 381-396, October.
    2. Mercedes Echeverria & David Stuart & Tobias Blanke, 2015. "Medical theses and derivative articles: dissemination of contents and publication patterns," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(1), pages 559-586, January.
    3. Vanja Pupovac, 2021. "The frequency of plagiarism identified by text-matching software in scientific articles: a systematic review and meta-analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(11), pages 8981-9003, November.

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