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

Ling Wei; Haiyun Xu; Zhenmeng Wang; Kun Dong; Chao Wang; Shu Fang

doi:10.20309/jdis.201626

Journal of Data and Information Science >

2016 , Vol. 1 >Issue 4: 81 - 101

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

Research Paper

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

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

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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

Received date: 2016-05-30

Revised date: 2016-09-12

Online published: 2016-09-12

Supported by

This 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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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.

Key words： Research topics; Weak tie network; Weak tie theory; Weak tie nodes; Library and Information Science (LIS)

Cite this article

Ling Wei , Haiyun Xu , Zhenmeng Wang , Kun Dong , Chao Wang , Shu Fang . 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 . DOI: 10.20309/jdis.201626

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