Preprints as accelerator of scholarly communication: An empirical analysis in Mathematics

TL;DR

This study empirically analyzes how preprints in mathematics accelerate scholarly communication by examining their impact on publication delay, citations, and social media attention, revealing significant early-view and open-access effects.

Contribution

It provides a comprehensive empirical analysis of preprints' role in enhancing scholarly communication, including metrics on impact, usage, and social media influence in mathematics.

Findings

Preprints lead to shorter publication delays.

Preprints increase citation counts and social media mentions.

Early-view and open-access effects significantly boost impact.

Abstract

In this study we analyse the key driving factors of preprints in enhancing scholarly communication. To this end we use four groups of metrics, one referring to scholarly communication and based on bibliometric indicators (Web of Science and Scopus citations), while the others reflect usage (usage counts in Web of Science), capture (Mendeley readers) and social media attention (Tweets). Hereby we measure two effects associated with preprint publishing: publication delay and impact. We define and use several indicators to assess the impact of journal articles with previous preprint versions in arXiv. In particular, the indicators measure several times characterizing the process of arXiv preprints publishing and the reviewing process of the journal versions, and the ageing patterns of citations to preprints. In addition, we compare the observed patterns between preprints and non-OA articles without any previous preprint versions in arXiv. We could observe that the "early-view" and "open-access" effects of preprints contribute to a measurable citation and readership advantage of preprints. Articles with preprint versions are more likely to be mentioned in social media and have shorter Altmetric attention delay. Usage and capture prove to have only moderate but stronger correlation with citations than Tweets. The different slopes of the regression lines between the different indicators reflect different order of magnitude of usage, capture and citation data.