An analysis of retracted papers in Computer Science

TL;DR

This study analyzes the prevalence, reasons, and citation patterns of retracted papers in Computer Science, highlighting issues like inconsistent retraction information and continued citations post-retraction, and calls for standardized procedures.

Contribution

It provides a comprehensive analysis of retracted CS papers, revealing disparities and citation behaviors, and emphasizes the need for standardized retraction practices.

Findings

8.3% of retracted papers are in CS

56% of CS retractions lack clear reasons

Citations to retracted papers continue long after retraction

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.