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Title: Topic Detection Based on Weak Tie Analysis: A Case Study of LIS Research
Author: Ling Wei1,2,3; Hai-yun Xu(许海云)1; Zhenmeng Wang1,2; Kun Dong1,2; Chao Wang1,2; Shu Fang(方曙)1
Source: Journal of Data and Information Science
Issued Date: 2016-11-03
Volume: 1, Issue:4, Pages:81-101
Keyword: Research topics ; Weak tie network ; Weak tie theory ; Weak tie nodes ; Library and Information Science (LIS)
Subject: 新闻学与传播学 ; 图书馆、情报与文献学
Indexed Type: 其他
DOI: 10.20309/jdis.201626
Corresponding Author: Haiyun Xu (E-mail:xuhy@clas.ac.cn)
DOC Type: Research Papers
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.
English 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.
Project Number: Grant No.:14CTQ033
Project: the National Social Science Youth Project "Study on the Interdisciplinary Subject Identification and Prediction"
Funder: This work is funded by the National Social Science Youth Project "Study on the Interdisciplinary Subject Identification and Prediction" (Grant No.:14CTQ033).
Related URLs: 查看原文
Language: 英语
Citation statistics:
Content Type: 期刊论文
URI: http://ir.las.ac.cn/handle/12502/8908
Appears in Collections:Journal of Data and Information Science_Journal of Data and Information Science-2016 _期刊论文

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description.institution: 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

Recommended Citation:
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.
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