# Self-citation Analysis using Sentence Embeddings

arXiv: https://arxiv.org/abs/2105.05527 · HTML: https://mlsanity.com/paper/2105.05527/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2021.

## Built on by (most cited first, 1 of 1)

- [Tracing the Genealogies of Ideas with Large Language Model Embeddings](https://mlsanity.com/paper/2402.01661/) (2024)

## Abstract (arXiv)

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.