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; Haiyun Xu (E-mail:xuhy@clas.ac.cn)
2016-11-03
Source PublicationJournal of Data and Information Science
Volume1Issue: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.
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.
; 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.
SubtypeResearch Papers
KeywordResearch 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
DOI10.20309/jdis.201626
URL查看原文
Indexed By其他
Project NumberGrant No.:14CTQ033
Language英语
Funding Projectthe National Social Science Youth Project "Study on the Interdisciplinary Subject Identification and Prediction"
Funding OrganizationThis work is funded by the National Social Science Youth Project "Study on the Interdisciplinary Subject Identification and Prediction" (Grant No.:14CTQ033).
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Document Type期刊论文
Identifierhttp://ir.las.ac.cn/handle/12502/8908
CollectionJournal of Data and Information Science_Journal of Data and Information Science-2016
Corresponding AuthorHaiyun Xu (E-mail:xuhy@clas.ac.cn)
Affiliation1.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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