<abstract xmlns="http://www.w3.org/1999/xhtml"><sec id="j_jdis.201626_s_007_w2aab2b8c62b1b7b1aab1c15b1Aa"><h3>Purpose</h3><p>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.</p></sec><sec id="j_jdis.201626_s_008_w2aab2b8c62b1b7b1aab1c15b2Aa"><h3>Design/methodology/approach</h3><p>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.</p></sec><sec id="j_jdis.201626_s_009_w2aab2b8c62b1b7b1aab1c15b3Aa"><h3>Findings</h3><p>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.</p></sec><sec id="j_jdis.201626_s_010_w2aab2b8c62b1b7b1aab1c15b4Aa"><h3>Research limitations</h3><p>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.</p></sec><sec id="j_jdis.201626_s_011_w2aab2b8c62b1b7b1aab1c15b5Aa"><h3>Practical implications</h3><p>The research is valuable for detecting important research topics as well as their roles, interrelations, and evolution trends.</p></sec><sec id="j_jdis.201626_s_012_w2aab2b8c62b1b7b1aab1c15b6Aa"><h3>Originality/value</h3><p>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.</p></sec></abstract>

PurposeBased 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/approachFirst, 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.FindingsThe 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 limitationsThe 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 implicationsThe research is valuable for detecting important research topics as well as their roles, interrelations, and evolution trends.Originality/valueTo 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.

Topic Detection Based on Weak Tie Analysis: A Case Study of LIS Research

Chengdu Documentation and Information Center

University of the Chinese Academy of Sciences

Journal of Data and Information Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Topic Detection Based on Weak Tie Analysis: A Case Study of LIS Research"}

PurposeBased 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,...

Topic Detection Based on Weak Tie Analysis: A Case Study of LIS Research

Article Category: Research Paper

Online veröffentlicht: 01. Sept. 2017

Seitenbereich: 81 - 101

Eingereicht: 30. Mai 2016

Akzeptiert: 12. Sept. 2016

DOI: https://doi.org/10.20309/jdis.201626

Schlüsselwörter
Research topics, Weak tie network, Weak tie theory, Weak tie nodes, Library and Information Science (LIS)

© 2016 Ling Wei, Haiyun Xu, Zhenmeng Wang, Kun Dong, Chao Wang, Shu Fang

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Topic Detection Based on Weak Tie Analysis: A Case Study of LIS Research

Article Category: Research Paper

Online veröffentlicht: 01. Sept. 2017

Seitenbereich: 81 - 101

Eingereicht: 30. Mai 2016

Akzeptiert: 12. Sept. 2016

DOI: https://doi.org/10.20309/jdis.201626

SchlüsselwörterResearch topics, Weak tie network, Weak tie theory, Weak tie nodes, Library and Information Science (LIS)

© 2016 Ling Wei, Haiyun Xu, Zhenmeng Wang, Kun Dong, Chao Wang, Shu Fang

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
Research topics, Weak tie network, Weak tie theory, Weak tie nodes, Library and Information Science (LIS)