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 like 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 dataset 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.
翻译:研究学者在传播社交媒体科学知识方面所发挥的作用一直是社交媒体衡量(计量)研究的一个中心议题,已采用不同方法在Twitter等社交媒体平台上识别和定性活跃学者,以往方法的一些局限性是其复杂性,最重要的是,它们依赖经许可的科学计量和对等数据。诸如OpenAlex或Crossref事件数据等新的开放数据源的出现为仅使用开放数据识别社交媒体学者提供了机会。本文介绍了一种新颖而简单的方法,将开放Alex的作者与Crossref事件数据中识别的Twitter用户相匹配。匹配程序与ORCID数据描述和验证。新的方法使近50万名匹配的学者与其Twitter账户相匹配,具有高度精确和中度的回调。匹配学者数据集被描述并公开提供给科学界,以便更深入地研究Twitter上研究人员的互动。