基于引文邻近位置的作者共被引分析  

The Author Co-citation Analysis Based on References Proximity Position

作　　者：孔月 李秀霞 Kong Yue;Li Xiuxia(School of Communication,QuFu Normal University,Rizhao,276826)

机构地区：[1]曲阜师范大学传媒学院

出　　处：《信息资源管理学报》2019年第4期37-44,共8页Journal of Information Resources Management

基　　金：国家社会科学基金项目“文献内容分析与引文分析融合的知识挖掘与发现研究”(16BTQ074)的研究成果之一

摘　　要：在共被引分析中,共被引作者在施引文献参考文献中被引用的距离对作者间的关系强度有一定的影响。为探析这种影响关系,改善作者共被引分析中缺乏位置信息的不足,将作者在施引文献的参考文献中被引用的位置信息加入到作者共被引分析中,提出了一种新的基于引文邻近位置的作者(RP-ACA),并以情报学领域5种CSSCI期刊上2008—2017年的文献为数据样本,确定核心作者37位,构建ACA矩阵和RP-ACA矩阵,运用Ucinet进行网络密度分析、利用VOSviewer进行聚类。通过聚类效果对比和关键词检验分析发现,与传统ACA方法比较,RP-ACA方法在识别作者研究方向、划分作者群上更加准确、细致。In the co-citation analysis,the distance cited by the co-cited authors in the citing literature has a certain influence on the relationship strength between authors.In order to explore this kind of influence relationship and improve the lack of position information in the author co-citation analysis,this paper adds the position information cited by the author in the citing literature to the author co-citation analysis.The analysis method is called"the author co-citation analysis based on references proximity position(RP-ACA)".The papers were sampled five CSSCI journals in the field of Information Science in 2008-2017.37 core authors were identified to constructing ACA matrix and RP-ACA matrix,then Ucinet was used for network density analysis,and VOSviewer was used for clustering.By comparing the clustering effect and keywords analysis,it is found that compared with the traditional ACA method,the RP-ACA method is more accurate and detailed in identifying the author's research direction and dividing the author group.

关 键 词：作者共被引分析 引文邻近位置 聚类分析 可视化 引文分析 学术评价 

分 类 号：G350[文化科学—情报学]