推特上学者的开放数据集

IF 4.1 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE
P. Mongeon, T. Bowman, R. Costas
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引用次数: 5

摘要

研究学者在社交媒体上科学知识传播中所扮演的角色一直是社交媒体度量(social media metrics, altmetrics)研究的中心话题。已经采用了不同的方法来识别和描述Twitter等社交媒体平台上活跃的学者。过去方法的一些局限性在于它们的复杂性,最重要的是,它们依赖于许可的科学计量学和替代计量学数据。新的开放数据源(如OpenAlex或Crossref Event data)的出现为仅使用开放数据在社交媒体上识别学者提供了机会。本文提出了一种新颖而简单的方法,将OpenAlex的作者与Crossref事件数据中识别的Twitter用户进行匹配。用ORCID数据描述和验证了匹配过程。这种新方法将近50万名学者与其Twitter账户匹配起来,准确率很高,记忆率适中。匹配学者的数据集被描述并公开提供给科学界,以便对研究学者在Twitter上的互动进行更高级的研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An open data set of scholars on Twitter
Abstract The role played by research scholars in the dissemination of scientific knowledge on social media has always been a central topic in social media metrics (altmetrics) research. Different approaches have been implemented to identify and characterize active scholars on social media platforms like Twitter. Some limitations of past approaches were their complexity and, most importantly, their reliance on licensed scientometric and altmetric data. The emergence of new open data sources such as OpenAlex or Crossref Event Data provides opportunities to identify scholars on social media using only open data. This paper presents a novel and simple approach to match authors from OpenAlex with Twitter users identified in Crossref Event Data. The matching procedure is described and validated with ORCID data. The new approach matches nearly 500,000 matched scholars with their Twitter accounts with a level of high precision and moderate recall. The data set of matched scholars is described and made openly available to the scientific community to empower more advanced studies of the interactions of research scholars on Twitter.
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来源期刊
Quantitative Science Studies
Quantitative Science Studies INFORMATION SCIENCE & LIBRARY SCIENCE-
CiteScore
12.10
自引率
12.50%
发文量
46
审稿时长
22 weeks
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