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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![]() ![]() ![]() | |
2016-11-03 | |
Source Publication | Journal of Data and Information Science
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Volume | 1Issue:4Pages:81-101 |
Abstract |
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. ; 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. 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. |
Subtype | Research Papers |
Keyword | Research Topics Weak Tie Network Weak Tie Theory Weak Tie Nodes Library And Information Science (Lis) |
Subject Area | 新闻学与传播学 ; 图书馆、情报与文献学 |
DOI | 10.20309/jdis.201626 |
URL | 查看原文 |
Indexed By | 其他 |
Project Number | Grant No.:14CTQ033 |
Language | 英语 |
Funding Project | the National Social Science Youth Project "Study on the Interdisciplinary Subject Identification and Prediction" |
Funding Organization | This work is funded by the National Social Science Youth Project "Study on the Interdisciplinary Subject Identification and Prediction" (Grant No.:14CTQ033). |
Citation statistics | |
Document Type | 期刊论文 |
Identifier | http://ir.las.ac.cn/handle/12502/8908 |
Collection | Journal of Data and Information Science_Journal of Data and Information Science-2016 |
Corresponding Author | Haiyun Xu (E-mail:xuhy@clas.ac.cn) |
Affiliation | 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 |
First Author Affilication | 中国科学院文献情报中心 |
Recommended Citation 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.,...&Haiyun Xu .(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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