[Sigmetrics] Tracing Digital Footprints to Academic Articles

王贤文Xianwen Wang xwang.dlut at gmail.com
Tue Jan 26 06:39:26 EST 2016

Dear colleagues,
It is a pleasure for me to announce our new study: “Tracing Digital
Footprints to Academic Articles: An Investigation of PeerJ Publication
Referral Data”

The preprint is available from arXiv: http://arxiv.org/abs/1601.05271
Tracing Digital Footprints to Academic Articles: An Investigation of PeerJ
Publication Referral Data <http://arxiv.org/abs/1601.05271>
Xianwen Wang, Shenmeng Xu and Zhichao Fang

In this study, we propose a novel way to explore the patterns of people's
visits to academic articles. About 3.4 million links to referral source of
visitors of 1432 papers published in the journal of PeerJ are collected and
analyzed. We find that at least 57% visits are from external referral
sources, among which General Search Engine, Social Network, and News & Blog
are the top three categories of referrals. Academic Resource, including
academic search engines and academic publishers' sites, is the fourth
largest category of referral sources. In addition, our results show that
Google contributes significantly the most in directing people to scholarly
articles. This encompasses the usage of Google the search engine, Google
Scholar the academic search engine, and diverse specific country domains of
them. Focusing on similar disciplines to PeerJ's publication scope, NCBI is
the academic search engine on which people are the most frequently directed
to PeerJ. Correlation analysis and regression analysis indicates that
papers with more mentions are expected to have more visitors, and Facebook,
Twitter and Reddit are the most commonly used social networking tools that
refer people to PeerJ.

Preprint is available at http://arxiv.org/abs/1601.05271


Xianwen Wang, Ph.D., Associate Professor
WISE Lab, Faculty of Humanities and Social Sciences
Dalian University of Technology
P.R. China

王贤文 博士、副教授
大连理工大学 人文与社会科学学部  WISE实验室,辽宁 大连 116085
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