追踪学术论文的数字足迹--基于PeerJ文献引用数据的研究
Tracing Digital Footprints to Academic Articles: An Investigation of PeerJ Publication Referral Data
摘要：
在这项研究中，我们提出了一种新的方法来探索人们对学术文章的访问模式。对《PeerJ》杂志上发表的1432篇论文的340万条访问者参考源链接进行了统计分析。我们发现，至少有57%的访问是来自外部的转介来源，其中一般搜索引擎，社会网络，新闻和博客是前三大类的转介。学术资源，包括学术搜索引擎和学术出版商的网站，是第四大类的参考来源。此外，我们的研究结果表明，谷歌在引导人们阅读学术文章方面的贡献最大。这包括使用谷歌的搜索引擎，谷歌学术搜索引擎，以及他们的不同特定国家域。NCBI专注于与PeerJ出版范围相似的学科，是人们最常指向PeerJ的学术搜索引擎。相关性分析和回归分析表明，被提及次数越多的论文，其访问量越大，Facebook、Twitter和Reddit是人们推荐PeerJ的最常用的社交网络工具。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
