2021/05/12 by Athanasios Lagopoulos, Grigorios Tsoumakas, Lagopoulos, Athanasios +1
Biochemistry, Genetics and Molecular Biology · Computer Science · Psychology · #Biomedical Text Mining and Ontologies #Citation #Computer science #Data science #Digital Libraries (cs.DL) #Economics #FOS: Computer and information sciences #Incentive #Law #Legitimacy #Natural Language Processing Techniques #Natural language processing #Political science #Psychology #Sentence #Topic Modeling #World Wide Web #cs.DL
paper · pdf · doi:10.48550/arxiv.2105.05527
published in arXiv (Cornell University) (Cornell University)
arxiv created 2021/05/12 · openalex publication_date 2021/05/12 · arxiv updated 2021/05/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.