论文标题
学术主题如何转移高度的来源？大数据研究领域的案例研究
How do academic topics shift across altmetric sources? A case study of the research area of Big Data
论文作者
论文摘要
我们将大数据的研究领域作为案例研究，我们提出了一种探索学术主题如何通过不同高级来源的受众之间的互动的方法。使用的数据是从Web of Science（WOS）和AltMetric.com获得的，重点关注博客，新闻，政策，Wikipedia和Twitter。从出版物和在线活动中提取的术语的作者关键字作为出版物的主要主题以及对Altmetric的受众的在线讨论。采取不同的措施来确定出版物作者提出的主题与在线受众提出的主题之间的相似性。结果表明，总体而言，围绕大数据科学研究的两组主题之间存在实质性差异。主要例外是Twitter，Twitter中的高频主题标签与作者在出版物中的关键字具有更强的一致性。在在线社区中，博客和新闻在常用的术语中表现出很强的相似性，而政策文件和Wikipedia文章在考虑和解释与大数据相关的研究方面表现出最强的差异。具体而言，观众不仅关注与社会或一般问题相关的更易于理解的学术主题，而且还将其扩展到在线讨论中的广泛主题。这项研究奠定了基础，以进一步调查在线观众在跨指标来源的学术主题转型以及在线社区对学术内容的关注程度和接受程度。
Taking the research area of Big Data as a case study, we propose an approach for exploring how academic topics shift through the interactions among audiences across different altmetric sources. Data used is obtained from Web of Science (WoS) and Altmetric.com, with a focus on Blog, News, Policy, Wikipedia, and Twitter. Author keywords from publications and terms from online events are extracted as the main topics of the publications and the online discussion of their audiences at Altmetric. Different measures are applied to determine the (dis)similarities between the topics put forward by the publication authors and those by the online audiences. Results show that overall there are substantial differences between the two sets of topics around Big Data scientific research. The main exception is Twitter, where high-frequency hashtags in tweets have a stronger concordance with the author keywords in publications. Among the online communities, Blogs and News show a strong similarity in the terms commonly used, while Policy documents and Wikipedia articles exhibit the strongest dissimilarity in considering and interpreting Big Data related research. Specifically, the audiences not only focus on more easy-to-understand academic topics related to social or general issues, but also extend them to a broader range of topics in their online discussions. This study lays the foundations for further investigations about the role of online audiences in the transformation of academic topics across altmetric sources, and the degree of concern and reception of scholarly contents by online communities.
