Topic Detection Based on Weak Tie Analysis: A Case Study of LIS Research
Article Category: Research Paper
Published Online: Sep 01, 2017
Page range: 81 - 101
Received: May 30, 2016
Accepted: Sep 12, 2016
DOI: https://doi.org/10.20309/jdis.201626
Keywords
© 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.
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.