Scientometric engineering: Exploring citation dynamics via arXiv eprints

TL;DR

This study analyzes over 1.5 million arXiv eprints to understand how open-access preprint dissemination influences citation patterns and attention dynamics across disciplines, revealing discipline-specific growth and obsolescence trends.

Contribution

It provides the first comprehensive quantitative analysis of arXiv citation dynamics and introduces a normalized citation index and stochastic model for cross-disciplinary comparison.

Findings

Computer Science eprints exhibit rapid growth and fast obsolescence.

Citation patterns vary significantly across disciplines.

The normalized citation index enables fair comparison of citation attention.

Abstract

Scholarly communications have been rapidly integrated into digitised and networked open ecosystems, where preprint servers have played a pivotal role in accelerating the knowledge transfer processes. However, quantitative evidence is scarce regarding how this paradigm shift beyond the traditional journal publication system has affected the dynamics of collective attention on science. To address this issue, we investigate the citation data of more than 1.5 million eprints on arXiv (https://arxiv.org/) and analyse the long-term citation trend for each discipline involved. We find that the typical growth and obsolescence patterns vary across disciplines, reflecting different publication and communication practices. The results provide unique evidence on the attention dynamics shaped by the research community today, including the dramatic growth and fast obsolescence of Computer Science eprints, which has not been captured in previous studies relying on the citation data of journal papers. Subsequently, we develop a quantitatively-and-temporally normalised citation index with an approximately normal distribution, which is useful for comparing citational attention across disciplines and time periods. Further, we derive a stochastic model consistent with the observed quantitative and temporal characteristics of citation growth and obsolescence. The findings and the developed framework open a new avenue for understanding the nature of citation dynamics.