Self-citation Analysis using Sentence Embeddings

TL;DR

This paper employs sentence embeddings to analyze journal self-citations in PubMed Central articles, aiming to differentiate between legitimate and unethical self-citations based on publication similarity.

Contribution

It introduces a large-scale, similarity-based approach to assess the legitimacy of self-citations in scientific articles.

Findings

Identified patterns distinguishing justifiable from unethical self-citations

Demonstrated the effectiveness of sentence embeddings in citation analysis

Provided insights into self-citation practices since 1990

Abstract

The purpose of citation indexes and metrics is intended to be a measure for scientific innovation and quality for researchers, journals, and institutions. However, those metrics are often prone to abuse and manipulation by excessive and unethical self-citations induced by authors, reviewers, editors, or journals. Identifying whether there are or not legitimate reasons for self-citations is normally determined during the review process, where the participating parts may have intrinsic incentives, rendering the legitimacy of self-citations, after publication, questionable. In this paper, we conduct a large-scale analysis of journal self-citations while taking into consideration the similarity between a publication and its references. Specifically, we look into PubMed Central articles published since 1990 and compute similarities of article-reference pairs using sentence embeddings. We examine journal self-citations with an aim to distinguish between justifiable and unethical self-citations.