Good practices for a literature survey are not followed by authors while preparing scientific manuscripts

TL;DR

This paper highlights that authors often neglect best practices in literature surveys, such as considering content similarity and systematic citation searches, which can bias citation metrics and impact scientific assessment.

Contribution

It advocates for two mandatory principles in literature review processes and demonstrates their neglect using complex network analysis on arXiv datasets.

Findings

Authors frequently ignore content similarity in citations.

Systematic citation network searches are rarely performed.

Neglect of these practices affects citation-based evaluations.

Abstract

The number of citations received by authors in scientific journals has become a major parameter to assess individual researchers and the journals themselves through the impact factor. A fair assessment therefore requires that the criteria for selecting references in a given manuscript should be unbiased with respect to the authors or the journals cited. In this paper, we advocate that authors should follow two mandatory principles to select papers (later reflected in the list of references) while studying the literature for a given research: i) consider similarity of content with the topics investigated, lest very related work should be reproduced or ignored; ii) perform a systematic search over the network of citations including seminal or very related papers. We use formalisms of complex networks for two datasets of papers from the arXiv repository to show that neither of these two criteria is fulfilled in practice.