Estimating the prevalence of text overlap in biomedical conference abstracts View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2021-02-01

AUTHORS

Nick Kinney, Araba Wubah, Miguel Roig, Harold R. Garner

ABSTRACT

BACKGROUND: Scientists communicate progress and exchange information via publication and presentation at scientific meetings. We previously showed that text similarity analysis applied to Medline can identify and quantify plagiarism and duplicate publications in peer-reviewed biomedical journals. In the present study, we applied the same analysis to a large sample of conference abstracts. METHODS: We downloaded 144,149 abstracts from 207 national and international meetings of 63 biomedical conferences. Pairwise comparisons were made using eTBLAST: a text similarity engine. A domain expert then reviewed random samples of highly similar abstracts (1500 total) to estimate the extent of text overlap and possible plagiarism. RESULTS: Our main findings indicate that the vast majority of textual overlap occurred within the same meeting (2%) and between meetings of the same conference (3%), both of which were significantly higher than instances of plagiarism, which occurred in less than .5% of abstracts. CONCLUSIONS: This analysis indicates that textual overlap in abstracts of papers presented at scientific meetings is one-tenth that of peer-reviewed publications, yet the plagiarism rate is approximately the same as previously measured in peer-reviewed publications. This latter finding underscores a need for monitoring scientific meeting submissions - as is now done when submitting manuscripts to peer-reviewed journals - to improve the integrity of scientific communications. More... »

PAGES

2

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s41073-020-00106-y

DOI

http://dx.doi.org/10.1186/s41073-020-00106-y

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1135021155

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/33517918


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