学术论文的Twitter和Mendeley指标的累积特征及规律探讨

Research on the Cumulation Characteristics and Laws of Twitter and Mendeley Collected in Research Article

通过对学术论文的推特(Twitter)和Mendeley的数据跟踪和分析,深入研究学术论文在网络媒体中的传播规律。跟踪记录了5种不同类型的期刊于2016年11-12月间发表的136篇学术论文的Twitter和Mendeley数据,数据收集截止到2017年12月中旬。重点探索两项指标的累积趋势和分布规律,计算两个时间节点的累积值与Scopus统计的引文次数之间的相关关系。结果显示,学术论文的Twitter和Mendeley累积值表现出一定程度的独立性,即论文不论是开放获取或非开放获取,无论出版机构是否性质相同,所发表的论文的Twitter和Mendeley这两项指标均可进行比较;两项指标不同时间节点的累积值具有一致性,并且具有早期预测功能,即可根据论文发表后其Twitter和Mendeley数据更快地发现和识别出高学术影响力论文。

Through the tracking and analysis of the Twitter and Mendeley data of the research article,this paper probes into the characteristics and laws of network dissemination of research article.The Twitter and Mendeley data of 136 research articles published in November 2016 issue on five different types of journals was tracked and recorded.The cumulative trends and distribution of the two indicators were explored;The correlation between two indicators data recorded at different stages and between two indicators data and citation data counted by Scopus were calculated.Twitter and Mendeley cumulative value of research article shows independence to a certain degree.This means that the articles,whether in form of open access and published by different publishers,can be compared by the two indicators.In addition.the distribution characteristics of the two indicators of different stages are consistent,which means that the two indicators have early prediction function that can used to identify high-impact research articles earlier.