Paper Info
Title
Tracing Digital Footprints to Academic Articles: An Investigation of PeerJ Publication Referral Data.
Abstract
In this study, we propose a novel way to explore the patterns of peopleu0027s 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 u0026 Blog are the top three categories of referrals. Academic Resource, including academic search engines and academic publishersu0027 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 PeerJu0027s 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.
Year
Venue
Field
2016
arXiv: Digital Libraries
Data science,World Wide Web,Social network,Computer science,Referral sources,Correlation analysis,Country code top-level domain,Tracing,Referral
DocType
Volume
Citations 
Journal
abs/1601.05271
2
PageRank 
References 
Authors
0.37
8
3
Name
Order
Citations
PageRank
Xianwen Wang122021.59
Shenmeng Xu214412.24
Zhichao Fang3363.92