Topic Detection Based on Weak Tie Analysis: A Case Study of LIS Research | |
Ling Wei1,2,3; Haiyun Xu1; Zhenmeng Wang1,2; Kun Dong1,2; Chao Wang1,2; Shu Fang1 | |
刊名 | journal of data and information science |
2016-11-03 | |
卷号 | 1期号:4页码:81-101 |
关键词 | Research topics Weak tie network Weak tie theory Weak tie nodes Library and Information Science (LIS) |
通讯作者 | haiyun xu (e-mail:xuhy@clas.ac.cn) |
中文摘要 |
purpose: based on the weak tie theory, this paper proposes a series of connection indicators of weak tie subnets and weak tie nodes to detect research topics, recognize their connections, and understand their evolution. design/methodology/approach: first, keywords are extracted from article titles and preprocessed. second, high-frequency keywords are selected to generate weak tie co-occurrence networks. by removing the internal lines of clustered sub-topic networks, we focus on the analysis of weak tie subnets' composition and functions and the weak tie nodes' roles. findings: the research topics' clusters and themes changed yearly; the subnets clustered with technique-related and methodology-related topics have been the core, important subnets for years; while close subnets are highly independent, research topics are generally concentrated and most topics are application-related; the roles and functions of nodes and weak ties are diversified. research limitations: the parameter values are somewhat inconsistent; the weak tie subnets and nodes are classified based on empirical observations, and the conclusions are not verified or compared to other methods. practical implications: the research is valuable for detecting important research topics as well as their roles, interrelations, and evolution trends. originality/value: to contribute to the strength of weak tie theory, the research translates weak and strong ties concepts to co-occurrence strength, and analyzes weak ties' functions. also, the research proposes a quantitative method to classify and measure the topics' clusters and nodes. |
英文摘要 | purpose: based on the weak tie theory, this paper proposes a series of connection indicators of weak tie subnets and weak tie nodes to detect research topics, recognize their connections, and understand their evolution. design/methodology/approach: first, keywords are extracted from article titles and preprocessed. second, high-frequency keywords are selected to generate weak tie co-occurrence networks. by removing the internal lines of clustered sub-topic networks, we focus on the analysis of weak tie subnets' composition and functions and the weak tie nodes' roles. findings: the research topics' clusters and themes changed yearly; the subnets clustered with technique-related and methodology-related topics have been the core, important subnets for years; while close subnets are highly independent, research topics are generally concentrated and most topics are application-related; the roles and functions of nodes and weak ties are diversified. research limitations: the parameter values are somewhat inconsistent; the weak tie subnets and nodes are classified based on empirical observations, and the conclusions are not verified or compared to other methods. practical implications: the research is valuable for detecting important research topics as well as their roles, interrelations, and evolution trends. originality/value: to contribute to the strength of weak tie theory, the research translates weak and strong ties concepts to co-occurrence strength, and analyzes weak ties' functions. also, the research proposes a quantitative method to classify and measure the topics' clusters and nodes. |
学科主题 | 新闻学与传播学 ; 图书馆、情报与文献学 |
收录类别 | 其他 |
原文出处 | http://www.jdis.org |
语种 | 英语 |
内容类型 | 期刊论文 |
源URL | [http://ir.las.ac.cn/handle/12502/8908] |
专题 | 文献情报中心_Journal of Data and Information Science_Journal of Data and Information Science-2016 |
作者单位 | 1.Chengdu Documentation and Information Center, Chinese Academy of Sciences, Chengdu 610041, China 2.University of the Chinese Academy of Sciences, Beijing 100049, China 3.School of Information Management, Shanxi University of Finance & Economics, Taiyuan 030006, China |
推荐引用方式 GB/T 7714 | Ling Wei,Haiyun Xu,Zhenmeng Wang,et al. Topic Detection Based on Weak Tie Analysis: A Case Study of LIS Research[J]. journal of data and information science,2016,1(4):81-101. |
APA | Ling Wei,Haiyun Xu,Zhenmeng Wang,Kun Dong,Chao Wang,&Shu Fang.(2016).Topic Detection Based on Weak Tie Analysis: A Case Study of LIS Research.journal of data and information science,1(4),81-101. |
MLA | Ling Wei,et al."Topic Detection Based on Weak Tie Analysis: A Case Study of LIS Research".journal of data and information science 1.4(2016):81-101. |
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