Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/24858
Title: An analysis of retracted papers in Computer Science
Authors: Shepperd, M
Yousefi, L
Keywords: cs.DL;cs.DL;cs.IR
Issue Date: 14-Jun-2022
Citation: Shepperd,M. and Yousefi, L. (2022) 'An analysis of retracted papers in Computer Science', 0, pp. 1-15, doi:
Abstract: Context: The retraction of research papers, for whatever reason, is a growing phenomenon. However, although retracted paper information is publicly available via publishers, it is somewhat distributed and inconsistent. Objective: The aim is to assess: (i) the extent and nature of retracted research in Computer Science (CS) (ii) the post-retraction citation behaviour of retracted works and (iii) the potential impact on systematic reviews and mapping studies. Method: We analyse the Retraction Watch database and take citation information from the Web of Science and Google scholar. Results: We find that of the 33,955 entries in the Retraction watch database (16 May 2022), 2,816 are classified as CS, i.e., approximately 8.3%. For CS, 56% of retracted papers, provide little or no information as to the reasons. This contrasts with 26% for other disciplines. There is also a remarkable disparity between different publishers, a tendency for multiple versions of a retracted paper over and above the Version of Record (VoR), and for new citations long after a paper is officially retracted. Conclusions: Unfortunately retraction seems to be a sufficiently common outcome for a scientific paper that we as a research community need to take it more seriously, e.g., standardising procedures and taxonomies across publishers and the provision of appropriate research tools. Finally, we recommend particular caution when undertaking secondary analyses and meta-analyses which are at risk of becoming contaminated by these problem primary studies.
Preprint version
URI: http://bura.brunel.ac.uk/handle/2438/24858
ISSN: 2331-8422
Other Identifiers: http://arxiv.org/abs/2206.06706v1
http://arxiv.org/abs/2206.06706v1
Appears in Collections:Dept of Computer Science Research Papers

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