Assessing the Dissemination of COVID-19 Articles Across Social Media With Altmetric and PlumX Metrics: Correlational Study

利用 Altmetric 和 PlumX 指标评估 COVID-19 文章在社交媒体上的传播:相关性研究

阅读:1

Abstract

BACKGROUND: The use of social media assists in the distribution of COVID-19 information to the general public and health professionals. Alternative-level metrics (ie, altmetrics) and PlumX metrics are new bibliometrics that can assess how many times a scientific article has been shared and how much a scientific article has spread within social media platforms. OBJECTIVE: Our objective was to characterize and compare the traditional bibliometrics (ie, citation count and impact factors) and new bibliometrics (ie, Altmetric Attention Score [AAS] and PlumX score) of the top 100 COVID-19 articles with the highest AASs. METHODS: The top 100 articles with highest AASs were identified with Altmetric Explorer in May 2020. The AASs, journal names, and the number of mentions in various social media databases of each article were collected. Citation counts and PlumX Field-Weighted Citation Impact scores were collected from the Scopus database. Additionally, AASs, PlumX scores, and citation counts were log-transformed and adjusted by +1 for linear regression, and Spearman correlation coefficients were used to determine correlations. RESULTS: The median AAS, PlumX score, and citation count were 4922.50, 37.92, and 24.00, respectively. The New England Journal of Medicine published the most articles (18/100, 18%). The highest number of mentions (985,429/1,022,975, 96.3%) were found on Twitter, making it the most frequently used social media platform. A positive correlation was observed between AAS and citation count (r(2)=0.0973; P=.002), and between PlumX score and citation count (r(2)=0.8911; P<.001). CONCLUSIONS: Our study demonstrated that citation count weakly correlated with AASs and strongly correlated with PlumX scores, with regard to COVID-19 articles at this point in time. Altmetric and PlumX metrics should be used to complement traditional citation counts when assessing the dissemination and impact of a COVID-19 article.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。